genetic algorithms research papers

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

An improved genetic algorithm and its application in neural network adversarial attack

Contributed equally to this work with: Dingming Yang, Zeyu Yu, Hongqiang Yuan

Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

Affiliation School of Computer Science, Yangtze University, Jingzhou, China

Roles Funding acquisition, Supervision, Validation, Writing – review & editing

Affiliation School of Electronic & Information, Yangtze University, Jingzhou, China

Roles Funding acquisition, Resources, Writing – review & editing

Affiliation School of Urban Construction, Yangtze University, Jingzhou, China

Roles Conceptualization, Project administration, Resources, Supervision, Writing – review & editing

* E-mail: [email protected]

• Dingming Yang, 
• Zeyu Yu, 
• Hongqiang Yuan, 
• Yanrong Cui

• Published: May 5, 2022
• https://doi.org/10.1371/journal.pone.0267970
• Reader Comments

The choice of crossover and mutation strategies plays a crucial role in the searchability, convergence efficiency and precision of genetic algorithms. In this paper, a novel improved genetic algorithm is proposed by improving the crossover and mutation operation of the simple genetic algorithm, and it is verified by 15 test functions. The qualitative results show that, compared with three other mainstream swarm intelligence optimization algorithms, the algorithm can not only improve the global search ability, convergence efficiency and precision, but also increase the success rate of convergence to the optimal value under the same experimental conditions. The quantitative results show that the algorithm performs superiorly in 13 of the 15 tested functions. The Wilcoxon rank-sum test was used for statistical evaluation, showing the significant advantage of the algorithm at 95% confidence intervals. Finally, the algorithm is applied to neural network adversarial attacks. The applied results show that the method does not need the structure and parameter information inside the neural network model, and it can obtain the adversarial samples with high confidence in a brief time just by the classification and confidence information output from the neural network.

Citation: Yang D, Yu Z, Yuan H, Cui Y (2022) An improved genetic algorithm and its application in neural network adversarial attack. PLoS ONE 17(5): e0267970. https://doi.org/10.1371/journal.pone.0267970

Editor: Mohd Nadhir Ab Wahab, Universiti Sains Malaysia, MALAYSIA

Received: November 24, 2021; Accepted: April 19, 2022; Published: May 5, 2022

Copyright: © 2022 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: D.Y., Z.Y., H.Y. and Y.C.; This work was supported by the Major Technology Innovation of Hubei Province [2019AAA011]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

In real life, optimization problems such as shortest path, path planning, task scheduling, parameter tuning, etc. are becoming more and more complex and have complex features such as nonlinear, multi-constrained, high-dimensional, and discontinuous [ 1 ]. Although a series of artificial intelligence algorithms represented by deep learning can solve some optimization problems, they lack mathematical interpretability due to the existence of a large number of nonlinear functions and parameters inside their models, so they are difficult to be widely used in the field of information security. Traditional optimization algorithms and artificial intelligence algorithms can hardly solve complex optimization problems with high dimensionality and nonlinearity in the field of information security.

Therefore, it is necessary to find an effective optimization algorithm to solve such problems. In this background, various swarm intelligence optimization algorithms have been proposed one after another, such as Particle Swarm Optimization(PSO) [ 2 , 3 ], Grey Wolf Optimizer(GWO) [ 4 ], etc. Subsequently, a variety of improved optimization algorithms also have been proposed one after another. For example, the improved genetic algorithm for cloud environment task scheduling [ 5 ], the improved genetic algorithm for flexible job shop scheduling [ 6 ], the improved genetic algorithm for green fresh food logistics [ 7 ], etc.

However, these improved optimization algorithms are improved for domain-specific optimization problems and do not improve the accuracy, convergence efficiency and generalization of the algorithms themselves. In this paper, the crossover operator and mutation operator of the genetic algorithm are improved to improve the convergence efficiency and precision of the algorithm without affecting the effectiveness of the improved genetic algorithm on most optimization problems. The effectiveness of the improved genetic algorithm is also verified through many comparison experiments and applications in the field of neural network adversarial attacks.

• By improving the single-point crossover link of SGA, the fitness function is used as an evaluation index for selecting children after crossover, thus reducing the number of iterations and accelerating the convergence speed.
• By improving the basic bitwise mutation of the SGA, traversing each gene of the offspring and performing selective mutation on them, setting different mutation rates for two parts of a chromosome, thus improving the global search in the stable case of local optimum.
• The improved genetic algorithm is applied to the field of neural network adversarial attack, which increases the speed of adversarial sample generation and improves the robustness of the neural network model.

2 Related works

2.1 genetic algorithm.

Genetic Algorithm is a series of simulation evolutionary algorithms proposed by Holland et al. [ 8 ], and later summarized by DeJong, Goldberg and others. The general flowchart of the Genetic Algorithm is shown in Fig 1 . The Genetic Algorithm first encodes the problem, then calculates the fitness, then selects the parent and the mother by roulette, and finally generates the children with high fitness by crossover and mutation, and finally generates the individuals with high fitness after many iterations, which is the satisfied solution or optimal solution of the problem. Simple Genetic Algorithm (SGA) uses single-point crossover and simple mutation to embody information exchange between individuals and local search, and does not rely on gradient information, so SGA can find the global optimal solution.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0267970.g001

2.2 Other meta-heuristic algorithms

The meta-heuristic algorithm is problem-independent, does not exploit the specificity of the problem, and is a general solution. In general, it is not greedy, can explore more search space, and tends to obtain the global optimum. To be more specific, meta-heuristic have one of the most important ideas: a dynamic balance mechanism between diversification and intensification.

The PSO [ 2 , 3 ] algorithm is a swarm intelligence-based global stochastic search algorithm inspired by the results of artificial life research and by simulating the migration and flocking behavior of bird flocks during foraging, and its basic idea is inspired by the results of research on modeling and simulation of birds flock behavior. The GWO algorithm is a swarm intelligence optimization algorithm proposed by Mirjalili et al. [ 4 ]. The algorithm is inspired by the grey wolf prey hunting activity and developed as an optimization search algorithm, which has strong convergence performance, few parameters, and easy implementation. The Marine Predator Algorithm (MPA) [ 9 ] is mainly inspired by foraging strategies widely found in marine predators, namely Lévy and Brownian motion, and optimal encounter rate strategies in biological interactions between predators and prey. The Artificial Gorilla Troops Optimizer (GTO) [ 10 ] was inspired by the gorilla group life behavior. The GTO is characterized by fast search speed and high solution accuracy. The African Vulture Optimization Algorithm(AVOA) [ 11 ] was inspired by the foraging and navigation behavior of African vultures. this algorithm is fast and has high solution accuracy which is widely used in single-objective optimization. The Remora Optimization Algorithm (ROA) [ 12 ] first proposed an intelligent optimization algorithm inspired by the biological habits of the neutrals in nature, which has good solution accuracy and high engineering practical value in both function seeking to solve extreme values and typical engineering optimization problems.

2.3 Neural network adversarial attack

Szegedy et al. [ 13 ] first demonstrated that a highly accurate deep neural network can be misled to make a misclassification by adding a slight perturbation to an image that is imperceptible to the human eye, and also found that the robustness of deep neural networks can be improved by adversarial training. Such phenomena are far-reaching and have attracted many researchers in the area of adversarial attacks and deep learning security. Akhtar and Mian [ 14 ] surveyed 12 attack methods and 15 defense methods for neural networks adversarial attacks. The main attack methods are finding the minimum loss function additive term [ 13 ], increasing the loss function of the classifier [ 15 ], the method of limiting the l_0 norm [ 16 ], changing only one pixel value [ 17 ], etc.

Nguyen et al. [ 18 ] continued to explore the question of “what differences remain between computer and human vision” based on Szegedy et al. [ 13 ]. They used the Evolutionary Algorithm to generate high-confidence adversarial images by iterating over direct-encoded images and CPPN (Compositional Pattern-Producing Network) encoded images, respectively. They obtained high-confidence adversarial samples (fooling images) using the Evolutionary Algorithm on a LeNet model pre-trained on the MNIST dataset [ 19 ] and an AlexNet model pre-trained on the ILSVRC 2012 ImageNet dataset [ 20 , 21 ], respectively.

Neural network adversarial attacks are divided into black-box attacks and white-box attacks. Black-box attacks do not require the internal structure and parameters of the neural network, and the adversarial samples can be generated with optimization algorithms as long as the output classification and confidence information is known. The study of neural network adversarial attacks not only helps to understand the working principle of neural networks but also increases the robustness of neural networks by training with adversarial samples.

3 Approaches

This section improves the single-point crossover and simple mutation of SGA. The fitness function is used as the evaluation index of the crossover link, and the crossover points of the whole chromosome are traversed to improve the efficiency of the search for the best. A selective mutation is performed for each gene of the children’s chromosome, and the mutation rate of the latter half of the chromosome is set to twice that of the first half to improve the global search under the stable situation of local optimum.

3.1 Improved crossover operation

As shown in algorithm 1 is the Python pseudocode for the improved crossover algorithm. The single-point crossover of SGA is to generate a random number within the parental chromosome length range, and then intercept the first half of the father’s chromosome and the second half of the mother’s chromosome to cross-breed the children according to the generated random number. In this paper, the algorithm is improved by trying to cross genes within the parental chromosome length range one by one, calculating the fitness, and picking out the highest fitness children individuals. Experimental data show that such an improvement can reduce the number of iterations and speed up the convergence of fitness.

Algorithm 1 Crossover with fitness as evaluation.

Input : Father’s gene, mother’s gene, fitness function;

Output : Child’s gene;

1: function CROSSOVER( father , mother , fitness )

2:   best _ fitness = float . MIN _ VALUE ;

3:   best _ child = np . zeros ( father . size );

4:   for i = 0 → father . size do

5:    current _ child = np . zeros ( father . size );

6:    current _ child = np . append ( father [0: i ], mother [ i :]);

7:    current _ fitness = fitness ( current _ child );

8:    if current _ fitness > best _ fitness then

9:     best _ fitness = current _ fitness ;

10:     best _ child = current _ child . copy ();

11:    end if

12:   end for

13:   return best _ child

14: end function

3.2 Improved mutation operation

As shown in algorithm 2 is the pseudocode of the improved mutation algorithm. The simple mutation of SGA sets a relatively large mutation rate, and mutates any one gene of the incoming children’s chromosome when the generated random number is smaller than the mutation rate. In this paper, we improve the algorithm by setting a small mutation rate and then selectively mutating each gene of the incoming children’s chromosome. That is, when the generated random number is smaller than the mutation rate, the gene is mutated, and when the traversed gene position is larger than half of the chromosome length, the mutation rate is set to twice the original one (the second half of the gene has relatively less influence on the result). This ensures that the first half of the gene and the second half of the gene have an equal chance of mutation respectively, and can mutate at the same time. When the gene length is 784, the mutation rate of the whole chromosome is 1 − (1 − 0.025) 392 × (1 − 0.05) 392 , which greatly improves the species diversity and at the same time ensures the stability of the species (in the stable situation of the local optimum improves the global search ability), and experimental data show that it can improve the search capability.

Algorithm 2 Mutate child with alter each gene if rand number less than mutate rate.

Input : Child’s gene;

Output : Mutated child’s gene;

1: function MUTATE( child )

2:   mutate _ rate = 0.025;

3:   for i = 0 → child . size do

4:    if i > child . size //2 then

5:     mutate _ rate = 0.05;

6:    end if

7:    if random . random () < mutate _ rate then

8:     child [ i ] = ! child [ i ];//child[i] equals 0 or 1

9:    end if

10:   end for

11:   return child

12: end function

4 Numerical experiments and analysis

4.1 test functions.

In order to evaluate the optimization performance of the proposed improved genetic algorithm, 15 representative test functions from AVOA paper of Abdollahzadeh et al. [ 11 ] and Wikipedia [ 22 ] are selected in this paper. Since the proposed improved genetic algorithm is mainly used for the neural network adversarial attack problem, and the neural network has multi-dimensional parameters, the dimensions of the test functions will be tested on 30, 50, and 100, respectively. The details of the formula, dimensions, range, and minimum of the 15 test functions are shown in Tables 1 – 3 , where Table 1 are multi-dimensional test functions with unimodal, Table 2 are multi-dimensional test functions with multi-modal, and Table 3 for fixed-dimensional test functions.

https://doi.org/10.1371/journal.pone.0267970.t001

https://doi.org/10.1371/journal.pone.0267970.t002

https://doi.org/10.1371/journal.pone.0267970.t003

4.2 Experimental environment

The hardware environment of the experiment includes 8G of RAM, i7–4700MQ CPU; the software environment includes Windows 10 system, and the version of Python is 3.8.8. In order to compare the optimization performance of IGA, SGA (Simple Genetic Algorithm), PSO (Particle Swarm Optimization) and GWO (Grey Wolf Optimizer) are selected as the experimental objects for comparison experiments in this paper.

(a) Mutation rate. (b) Population size. (c) Max iteration.

https://doi.org/10.1371/journal.pone.0267970.g002

https://doi.org/10.1371/journal.pone.0267970.t004

4.3 Experimental results and analysis

4.3.1 qualitative result analysis..

(a) Parameter space. (b) Population distribution. (c) Best record. (d) Convergence curve.

https://doi.org/10.1371/journal.pone.0267970.g003

https://doi.org/10.1371/journal.pone.0267970.g004

https://doi.org/10.1371/journal.pone.0267970.g005

https://doi.org/10.1371/journal.pone.0267970.g006

4.3.2 Quantitative result analysis.

In order to make a quantitative comparison with the other three mainstream optimization algorithms, the four optimization algorithms are performed independently for 10 experiments on F1-F11 test functions in dimensions 30, 50, and 100, respectively. The purpose of performing the high-dimensional function test is to test the convergence superiority of IGA on the high-dimensional space for application in the field of neural network adversarial attack. Tables 5 – 7 are the test results of the test functions F1-F11 in 30, 50, and 100 dimensions, respectively. Table 8 shows the results of the four optimization algorithms tested on the test functions F12-F15. The best result, worst result, mean, median, standard deviation, and P-value are compared for 10 experiments. Where P-value is the result of the Wilcoxon rank-sum statistical test and P-value below 5% is significant.

https://doi.org/10.1371/journal.pone.0267970.t005

https://doi.org/10.1371/journal.pone.0267970.t006

https://doi.org/10.1371/journal.pone.0267970.t007

https://doi.org/10.1371/journal.pone.0267970.t008

In Table 5 , IGA achieves significantly superior performance in 9 test functions, PSO is better in F3, and SGA is slightly better in F8. In Tables 6 and 7 , IGA achieves significantly superior performance in 10 test functions, PSO performs better in F3. It can be seen that the performance loss of IGA with increasing dimensionality is not as large as the other three optimization algorithms. In Table 8 , IGA achieves significantly superior performance in 3 test functions, and PSO performs slightly better in F14.

In general, IGA has better iteration efficiency, global search capability, and convergence success rate than the other three optimization algorithms.

5 Application in neural network adversarial attack

5.1 mnst dataset.

The MNST dataset (Mixed National Institute of Standards and Technology database) [ 19 ] is one of the most well-known datasets in the field of machine learning and is used in applications from simple experiments to published paper research. It consists of handwritten digital images from 0–9. The MNIST image data is a single-channel grayscale map of 28 × 28 pixels, with each pixel taking values between 0 and 255, with 60,000 samples in the training set and 10,000 samples in the test set. The general usage of the MNIST dataset is to learn with the training set first and then use the learned model to measure how well the test set can be correctly classified [ 23 ].

5.2 Implementation

As shown in Fig 7(a) , the Deep Convolutional Neural Network (DCNN) pre-trained on the MNST dataset [ 19 ] is used as the experimental object in this paper, and the accuracy of the model is 99.35% with a Loss value of 0.9632. As shown in Fig 7(b) , the model of network adversarial attack is shown. The number of populations of a specific size (set to 100 in this paper) is first generated and then input to the neural network to obtain the confidence of the specified labels. To reduce the computational expense, the input is reduced to a binary image of 28 × 28 and the randomly generated binary image is iterated using the IGA proposed in this paper. Among the 100 individuals, the fathers and mothers with relatively high confidence are selected by roulette selection, and then the children are generated by using the improved crossover link in this paper, and the children from a new population by improving the mutation link until the specified number of iterations. Finally, the individual with the highest confidence is picked from the 100 individuals, which is the binary image with the highest confidence after passing through the neural network.

(a) The structure of DCNN for experiment. (b) The model of network adversarial attack.

https://doi.org/10.1371/journal.pone.0267970.g007

As shown in Fig 8 , the confidence after 99 iterations of DCNN is 99.98% for sample “2”. Sample “6” and sample “4” have the slowest convergence speed, and the confidence of sample “6” is 78.84% after 99 iterations, and the confidence of sample “4” is 78.84% after 99 iterations.

https://doi.org/10.1371/journal.pone.0267970.g008

The statistics of the experimental results are shown in Fig 9 . The binary image of sample “1” generated after 999 iterations has confidence of 99.94% after passing DCNN, which is much higher than the confidence of sample “1” in the MNIST test set in the DCNN control group. In the statistics of the results after initializing the population with the MNIST test set, because the overall confidence of the population initialized with the test set is higher, the increase in confidence during iteration is smaller. The confidence of the sample selected from the MNIST test set is 99.56%, and after 10 iterations the confidence of the sample is 99.80%, and the number “1” becomes vertical; after 89 iterations the confidence is 99.98%, and the number “1” has a tendency to “decompose” gradually.

https://doi.org/10.1371/journal.pone.0267970.g009

As shown in Fig 10 , the reason for this situation is probably that the confidence as a function of the image input is a multi-peak function, and the interval in which the test set images are distributed is not the highest peak of the confidence function. This causes the initial population of the test set to “stray” from some pixels in the images generated by the IGA.

https://doi.org/10.1371/journal.pone.0267970.g010

6 Conclusion

The comparison and simulation experiments show that the improved method proposed in this paper is effective and greatly improves the convergence efficiency, global search capability and convergence success rate. Applying IGA to the field of neural network adversarial attacks can also quickly obtain adversarial samples with high confidence, which is meaningful for the improvement of the robustness and security of neural network models.

In this paper, although the genetic algorithm has been improved to enhance the performance of the genetic algorithm, it is based on the genetic algorithm, so it cannot be completely separated from the general framework of the genetic algorithm, and the problem that the genetic algorithm is relatively slow in a single iteration cannot be solved. We hope to explore a new nature-inspired optimization algorithm in our future work. In addition, the reason why the neural network model has so many adversarial samples, we believe that it is a design flaw in the architecture of the neural network model. In future work, we will also try to explore a completely new way of the infrastructure of neural networks so as to compress the space of adversarial samples.

With the wide application of artificial intelligence and deep learning in the field of computer vision, face recognition has outstanding performance in access control systems and payment systems, which require a fast response to the input face image, but this has instead become a drawback to be hacked. For face recognition systems without in vivo detection, using the method in this paper only requires output labels and confidence information can obtain high confidence images quickly. In summary, neural networks have many pitfalls due to their uninterpretability and still need to be considered carefully for use in important areas.

Supporting information

https://doi.org/10.1371/journal.pone.0267970.s001

• View Article
• Google Scholar
• 2. Eberhart R. and Kennedy J. (1995). A new optimizer using particle swarm theory. In MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science , pages 39–43. Ieee.
• 3. Kennedy J. and Eberhart R. (1995). Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural networks , volume 4, pages 1942–1948. IEEE.
• 8. Holland J. H. et al. (1975). Adaptation in natural and artificial systems.
• 13. Szegedy C., Zaremba W., Sutskever I., Bruna J., Erhan D., Goodfellow I., et al. (2013). Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 .
• 15. Kurakin A., Goodfellow I., Bengio S., et al. (2016). Adversarial examples in the physical world.
• 16. Papernot N., McDaniel P., Jha S., Fredrikson M., Celik Z. B., and Swami A. (2016). The limitations of deep learning in adversarial settings. In 2016 IEEE European symposium on security and privacy (EuroS&P) , pages 372–387. IEEE.
• 18. Nguyen A., Yosinski J., and Clune J. (2015). Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 427–436.
• 19. LeCun Y. (1998). The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist/ .
• 20. Deng J., Dong W., Socher R., Li L.-J., Li K., and Fei-Fei L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition , pages 248–255. Ieee.
• 22. Wikipedia (2021). Test functions for optimization. Website. https://en.wikipedia.org/wiki/Test_functions_for_optimization .
• 23. Yasue S. (2018). Deep Learning from Scratch . “Beijing: Posts and Telecom Press”.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.30(6); 2015 Nov

The Applications of Genetic Algorithms in Medicine

Ali ghaheri.

1 Department of Management and Economy, Science and Research Branch, Azad University, Tehran, Iran

Saeed Shoar

2 Department of Surgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran

Mohammad Naderan

3 School of Medicine Tehran University of Medical Sciences, Tehran, Iran

Sayed Shahabuddin Hoseini

4 Hannover Medical School, Germany

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.]

Introduction

There is no doubt that computers have revolutionized our everyday life. They are vastly used and have benefited nearly all fields of science from aerospace and astronomy to biology, chemistry, physics, mathematics, geography, archeology, engineering, and social sciences.

In medicine, electronic chips and computers are the backbones of a lot of imaging, diagnostic, monitoring, and therapeutic devices. These devices, which are composed of several different hardware components, are managed and controlled by software, which in turn are based on algorithms. An algorithm is a set of well-described rules and instructions that define a sequence of operations. Metaheuristic methods are algorithms that can more quickly solve complex problems, or they can find an approximate solution when classical methods are not able to find an exact one. 1

Several metaheuristic algorithms for finding an optimal or near-optimal solution exist. These include the ant colony (inspired by ants behavior), 2 artificial bee colony (based on bees behavior), 3 Grey Wolf Optimizer (inspired by grey wolves behavior), 4 artificial neural networks (derived from the neural systems), 5 simulated annealing, 6 river formation dynamics (based on the process of river formation), 7 artificial immune systems (based on immune system function), 8 and genetic algorithm (inspired by genetic mechanisms). 9 Metaheuristic approaches have been frequently used in other fields of science where complex problems need to be solved, or optimal decisions should be made. In medicine, although valuable work has been done, the power of these potent algorithms for offering solutions to the countless complex problems physicians encounter every day has not been fully exploited.

In this paper, we introduce the genetic algorithm (GA) as one of these metaheuristics and review some of its applications in medicine.

The genetic algorithm

A GA is a metaheuristic method, inspired by the laws of genetics, trying to find useful solutions to complex problems. In this method, first some random solutions (individuals) are generated each containing several properties (chromosomes). Based on the laws of genetics, cross-over and mutations occur in chromosomes to produce a second generation of individuals with more diverse properties.

Crossover and mutation are the two most central methods for diversifying individuals. In crossover, two chromosomes are chosen. Then a crossover point along each chromosome is chosen followed by the exchange of the values up to the crossover point between the two chromosomes [Figure 1]. These two newly-generated chromosomes produce new offspring. The process of crossover will be iterated over and over until the desired diversity of individuals (i.e. solutions) is made. The mutation also generates new configurations by applying random changes in different chromosomes. 10 One of the simplest mutation methods has been depicted in Figure 1.

Methods to induce diversity in the population of individuals (candidate solutions). (a) During crossover, one part of a chromosome is exchanged by another fragment of another chromosome. (b) During mutations, one or more datasets on a chromosome are converted to different ones. These alterations will generate new individuals whose fittest (more optimal solutions) will survive.

In a GA, the possibility of reproduction depends on the fitness of individuals. The better chromosomes they have (i.e., those with better characteristics), the more likely they are to be selected for breeding the next generation. There are several selection methods; however, the aim of all is to assign fitness values to individuals based on a fitness function and to select the fittest. Genetic alterations in chromosomes will happen via crossover and mutations to produce another generation. This iterative process will continue until the fittest individual (the optimal solution) is formed or the maximum number of generations is reached. 9 , 11

It is worth noting that GAs are different from the derivative-based, optimization algorithms. First of all, GAs search a population of points in the solution space in each iteration while classical derivative-based methods search only a single point. Moreover, GAs select the next population using probabilistic transition rules and random number generators while derivative-based algorithms use deterministic transition rules for selecting the next point in the sequence. 11 , 12

In the following, we introduce some of the applications of GAs in a variety of medical disciplines.

Imaging techniques in radiology generate a large amount of data that needs to be analyzed and interpreted by radiologists in a relatively short time. Computer-aided detection and diagnosis are rapidly growing interdisciplinary technologies that aim to assist radiologists in faster and more accurate image analysis by detection, segmentation, and classification of normal and pathological patterns found on various imaging modalities. These include X-rays, magnetic resonance imaging (MRI), compute tomography (CT) scan, and ultrasound. 13

In machine vision, an image of scenery (such as organs of the human body in radiology images) is acquired, processed, and interpreted. The boundaries (shape) and sizes of objects within the images need to be determined to assess the objects in more detail. Therefore, the process of edge detection becomes one of the integral parts of automatic image processing techniques. 14 Several researchers have used the GAs for edge detection of images acquired using different imaging modalities including MRI, CT, and ultrasound. 14 - 16

Screening mammography is the gold standard for detection of breast cancer; however, due to its failure rate, 17 , 18 researchers have tried to apply computational tools to improve the sensitivity of the system. In fact, the majority of the applications of GAs in radiology were performed on breast cancer screening primarily using mammography.

Karnan and Thangavel 19 applied the GA to detect microcalcifications in mammograms suggesting of breast cancer. In their method, after enhancement and normalization of the mammograms, the border of breast and the nipple position was detected by the GA. Using the border and the nipple position of the right and left breasts as a reference, the mammogram images were aligned and subtracted from each other to find the asymmetry image suggestive of breast cancer. The Az value, which is the area under the receiver operating characteristic (ROC) curve, has been used as a useful measure for assessing the diagnostic performance of a system. 20 The Az value for their proposed algorithm was about 0.9. 19

In another study, Pereira et al, 21 applied a set of computational tools for mammogram segmentation to improve the detection of breast cancer. An algorithm was first designed to eliminate artifacts followed by denoising and image enhancement. Consecutively, combining wavelet analysis and the GA allowed detection and segmentation of suspicious areas with 95% sensitivity. GAs have also been successfully used for classification and detection of clustered microcalcifications in digital mammograms. 22 - 24

In machine learning, feature selection is the process of selecting a subset of relevant features to construct a model by removing variables with little or no analytical value. Feature selection is important since choosing irrelevant features would increase the time, cost, and complexity of computation and reduce the accuracy of the model. 25 Besides, reducing the number of features would avoid the problem of over-fitting, reduce the chance of failure upon missing data, and allow for a better explanation and generalization of the model. 26

GAs have been applied for feature selection in studies aiming to identify a region of interest in mammograms as normal or containing a mass, 27 and to differentiate benign and malignant breast tumors in ultrasound images. 25

de Carvalho Filho et al, 28 developed a GA for automatic detection and classification of solitary lung nodules. The designed algorithm could detect lung nodules with about 86% sensitivity, 98% specificity, and 98% accuracy.

Image registration or fusion is the process of optimal aligning of two or more images into one coordinate system. Precise integration of images becomes crucial when valuable information is embedded within several images acquired under different conditions (viewpoint, sensor, or time). 29 GAs have successfully been used to align MRI and CT scan images in several studies. 30 , 31 In another study, positron emission tomography (PET) images were fused with MRI images by a GA to generate colored breast cancer images. 32

Precise tumor staging is an important part of designing a treatment plan. Accurate tumor size and volume determination using non-invasive imaging studies becomes essential for tumor staging. Zhou et al, 33 developed a system for extraction of tongue carcinoma from head and neck MRIs. A GA was applied for segmentation of images followed by an artificial neural network (ANN)-based symmetry-detection algorithm to reduce the number of false positive results. This approach was able to extract tongue carcinoma from an MRI with high accuracy and minimal user-dependency.

Screening tests offer a valuable opportunity for early cancer detection, which if followed by proper treatment could improve the survival rate of patients.

To develop a non-invasive technique for cervical cancer detection, Duraipandian et al, 34 acquired Raman spectra from the cervical area via colposcopy. The biomolecular information generated via the Raman spectroscopy was analyzed by a GA-partial least square-discriminant analysis system to differentiate between a normal and dysplastic cervix. Partial least square (PLS) is a statistical method aiming to find a linear regression model between a dependent variable and some predictor variables. 35 This system was able to differentiate dysplasia from a normal cervix with 72% sensitivity and 90% specificity. 34

The advent of DNA microarrays has paved the way for massive gene expression profiling that could revolutionize the field of molecular diagnostics and prognosis. However, generation of large sets of data poses statistical and analytical challenges necessitating the need to find key predictive genes. 36 Due to the inherent capability of GAs to search and find the optimal solution among large and complex possible solutions with multiple simultaneous interactions, they have been applied to analyze microarray data from several cancer cell lines. 36 Dolled-Filhart et al, 37 generated microarray data by staining breast cancer tissues with several antibodies specific for various markers to find a minimum set of biomarkers with maximum classification and prognostication values in breast cancer patients. The data analyzed using GAs showed that three markers with available antibodies could define a population of patients with more than a 95% five-year survival rate.

Tan et al, 38 conducted a study to investigate the relationship between soil trace elements and cervical cancer mortality in China. A combination of GA and PLS was used to choose five out of 25 trace elements. Then a least square support vector machine (LSSVM) model was developed. LSSVM is a method used in machine learning to infer a function from or find a pattern in training data. 39 The results showed that a combination of GA-PLS and LSSVM could predict the mortality of cervical cancer based on trace elements. 38

One of the important and informative factors influencing the choice of an appropriate therapeutic approach for cancer patients is determination of the disease prognosis. In a retrospective study on more than 200 patients, Bozcuk et al, 40 compared the performance of four different data mining methods to determine the outcome of cancer patients not being in terminal stages after hospitalization. In comparison to other methods, GA selected the least number of explanatory variables (lactate dehydrogenase and the reason for admission) to predict the outcome of patients.

GAs have been used in different fields of cardiovascular medicine. Atherosclerotic plaques are hallmarks of most myocardial infarctions and strokes. Determination of plaque mechanical properties such as elasticity would enable physicians to locate better and map vulnerable or unstable plaques. Khalil et al, 41 used a system involving GAs for parameter estimation necessary for accurate elasticity quantification to determine tissue elasticity. This system is superior to gradient-based methods used for parameter estimation of the inefficiency of gradient-based techniques for inhomogeneous solution spaces containing several local minima and requirement for substantial computational time limits their application. 41

The field of biomarker discovery and clinical proteomics is rapidly growing in medical diagnosis, prognosis, and disease follow-up. Advanced technologies such as mass spectrometry can generate readouts of thousands of proteins from patient samples; however, the cost and complexity of such techniques on the one hand and computational and statistical methods for analysis, on the other hand, necessitates the selection of a few, relevant markers for clinical assay development. Zhou et al, 42 employed an improved version of the GA supported by a recursive local floating enhancement technique to predict the risk of a major adverse cardiac event (MACE). This technique was able to select a panel of seven proteins including myeloperoxidase to predict the risk of MACE with 77% accuracy, which outperformed over several current methods.

Logistic regression models have been frequently used in diagnosing diseases. Due to its outstanding performance, a GA has been used to select the best variables for a logistic regression system aiming to model the presence of myocardial infarction in patients with chest pain. The GA-based method was superior in variable selection to other traditional methods. 26

One of the key elements in the automatic interpretation of the electrocardiogram (ECG) is the detection of QRS complexes that would allow assessment of heart rate variability and other relevant diagnostic parameters. Tu et al, 43 introduced a simple and effective GA to detect QRS complexes. Then, p-waves and f-waves, which happen in normal ECG and after atrial fibrillation, respectively, were successfully extracted from patient databases. Such algorithms could allow comprehensive research into ECG details.

Endocrinology

Hypoglycemia is the most common complication of insulin therapy in patients with type 1 diabetes mellitus (T1DM). Hypoglycemia can induce alterations in the patterns of electroencephalograms (EEGs). Nguyen et al, 44 combined ANNs, GAs, and Levenberg-Marquardt (LM) training techniques to detect hypoglycemia based on EEG signals. ANN was used to model the relationship between blood glucose and EEG signals. For training ANN, the global search ability of GA and the local search capability of LM were combined. Data from four EEG parameters derived from two EEG channels were used by the analyzing system to detect hypoglycemia with 75% sensitivity and 60% specificity. In another paper, a GA-based multiple regression with fuzzy inference system was developed to detect non-invasive episodes of nocturnal hypoglycemia in children with T1DM. Using heart rate and corrected QT interval, hypoglycemia was detected with a sensitivity of 75% and specificity of over 50%. 45

Obstetrics and gynecology

The differentiation between normal and prolonged delivery allows obstetricians to determine the optimal timing for interventions, if necessary, during childbirth. One of the parameters that can help to forecast the delivery time and segregate normal versus prolonged labor is the time to reach full cervical dilation. Hoh et al, 46 applied a three-parameter logistic model using GA or the Newtone-Raphson (NR) method to predict the time to reach full cervical dilation. The GA-based algorithm outperformed the NR method by more accurately predicting the time to full cervical dilation.

A Pap smear is a cytology test for detection of precancerous and cancerous cervical changes. In this method, 20 features of cells are assessed to describe them as normal or abnormal or, more specifically, categorize them into seven classes. Marinakis et al, 47 generated a hybrid model that took advantage of the feature-selection capability of GAs to reduce the complexity of features necessary for a nearest neighbor algorithm for classification of Pap smear results. The new method outperformed several other previously used approaches by accurately classifying the Pap smear results.

GAs have also been applied in prenatal diagnosis. One of the fetal features that can complicate delivery is fetal macrosomia. In an attempt to differentiate the large-for-gestational-age (LGA) from the appropriate-for-gestational-age (AGA) infants, amniotic fluid from the second trimester was evaluated by capillary electrophoresis. Bayesian statistics was applied for data analysis. A GA was used to select the suitable wavelets (variables) of the electropherogram to minimize the computation time required for the Bayesian computation. This system was able to differentiate LGA from AGA using only two wavelets, one of albumin and the other of a negatively-charged unknown small molecule with 100% sensitivity and 98% specificity. 48

The prediction of fetal weight before delivery can reduce the potential problems associated with low-birth-weight infants. Yu et al, 49 introduced fuzzy logic into the support vector regression (FSVR) to estimate the fetal weight. GAs were used to generate an evolutionary FSVR to select the optimal features for the FSVR system. This outperformed a back-propagation neural network by achieving the lowest mean absolute percent error (6.6%) and the highest correlation coefficient (0.902) between the estimated and the actual fetal birth weight.

Cardiotocography is a cheap and non-invasive technique to assess the fetal heart rate and uterine contractions to determine fetal well-being. Ocak 50 applied a GA to select the optimal features of cardiotocogram recordings for a support vector machine (SVM) classifier. The results showed that the new system classified fetal health status as normal or abnormal with 99.3% and 100% accuracy, which was superior to an ANN algorithm designed for the same purpose.

Autism is a neurodevelopmental disease that appears in early childhood and is characterized by impaired social functioning and verbal and non-verbal communications and repetitive behavior. To recognize autism based on the microarray gene expression data, Latkowski and Osowski 51 used GAs to select the most relevant genes associated with the disease. Frequently selected genes include RMI1, NRIP1, TOP1, ZFHX3, CEP350, NFYA, PSENEN, ANP32A, SEMA4C, and SP1. These genes provided an input for an ensemble of classifiers including SVM and random forest classifiers. The introduced system recognized autism with 96% sensitivity and 83% specificity. 51

Acute lymphoblastic leukemia (ALL) is the most common type of leukemia in children and has many subtypes. Analysis of gene expression data derived from tumor cells can help classifying cancers. Due to the enormous size of information generated from microarray gene expression profiling, Lin et al, 52 used a GA to select the most relevant genes needed for ALL classification. Silhouette statistics was applied as a discriminant function to differentiate between six ALL subtypes. The proposed technique reached a 100% classification accuracy and used fewer discriminating genes compared to other methods.

Aneuploidy is a condition where one or a few chromosomes in the nucleus of a cell are above or below the normal chromosomal number of a species. Conventional chromosomal studies on amniocentesis samples are performed for definite diagnosis of fetal aneuploidy yet the rather long required time for these techniques necessitates the development of faster diagnostic tests. To this end, the proteomic profile of the amniotic fluid specimens was identified via mass spectrometry and the generated data was assessed by a GA. The proposed method could detect aneuploidy with 100% sensitivity, 72%–96% specificity, 11%–50% positive predictive value and 100% negative predictive value. 53

ANNs are powerful mathematical algorithms capable of predicting the behavior of systems. Due to the predictive value of ANNs, a GA-based ANN (GANN) was developed to predict the outcomes after surgery for patients with non-small cell lung cancer (NSCLC). The GA was applied to help optimization not to fall into local minima. The GANN model could predict the outcome of NSCLC patients more accurately and significantly better than logistic regression. Besides, the inclusion of tumor size in calculations significantly improved prediction outcomes. 54

As populations age, the number of geriatric patients needing cardiac surgeries increases. Due to the high prevalence of comorbid conditions in elderly, proper prognostication of postoperative morbidity and mortality would be informative, precluding overestimation of risk and denial of surgery for patients deserving it, which could happen with some prediction models. Applying a GA, Lee et al, 55 showed that a short length of stay after cardiac surgery was correlated with younger age, no preoperative use of beta blockers, shorter cross-clamp time, and absence of congestive heart failure.

Pulmonology

In pulmonology, auscultation is the most common diagnostic method that can differentiate lung diseases and guide the diagnostic approach toward more specific techniques. To automate lung sound diagnosis, a hybrid GANN was designed. The GA was applied to optimize the ANN training parameters and reduce the computation time. The new system could classify the lung sounds into normal, wheeze, and crackle. 56

Assessment of the partial pressure of carbon dioxide in the arterial blood (PaCO 2 ) is important in the management of critically ill patients. To avoid difficulties associated with arterial blood sampling, non-invasive methods for predicting PaCO 2 such as assessment of exhaled carbon dioxide at end-expiration (PetCO 2 ) could be applied in normal individuals; however, their use in sicker persons might be biased and less helpful. Engoren et al, 57 designed a GA to predict the PaCO 2 using 11 variables from capnography of non-intubated patients in the emergency department. The proposed system could improve the precision and bias of PaCO 2 prediction.

Infectious diseases

Tuberculosis is a possible lethal infectious disease not only in developing countries but also in developed nations after the emergence of human immunodeficiency virus (HIV). To predict the diagnosis (tuberculosis vs. non-tuberculosis patients), 38 parameters composed of examination parameters and laboratory data were used to design an ANN trained by a GA. The classification accuracy of the system was about 95%, which was higher than the results obtained by other algorithms. 58

Highly active antiretroviral therapy (HAART), an integral part of the treatment modalities against HIV, is composed of a combination of several antiretroviral medications aiming to decrease the replication of the virus. Since long-term HAART treatment needs patient compliance and might be associated with some side effects, structured treatment interruption has been proposed to reduce not only side effects, but also the selection pressure on the virus that could lead to the emergence of resistant particles. Therefore, Castiglione et al, 59 devised a GA-based system to choose the best HAART treatment schedule to control HIV and help the immune system to reconstitute. A virtual model of the immune system was used to assess the effects of anti-HIV drugs on virtual patients. 59 , 60 The new structured interruption schedule could achieve therapeutic results and protection against an opportunistic infection comparable to a full-length treatment. 61

Radiotherapy

Intensity modulated radiotherapy (IMRT) was developed to transfer an accurate dose of radiation to a target such as the brain, prostate, or head and neck. Planning IMRT involves selection of 5–10 angles for wavelet projection and determining the radiation dose. The application of GA could improve the selection of gantry angles in a reasonable time frame. 62 Similar GA-based irradiation planning has been applied for patients with other types of cancer including pancreatic, 63 rhabdomyosarcoma, and brain tumors. 64 GAs have also been successfully used to optimize the design of stereotactic radiosurgery, and radiotherapy treatment plans. 65

Rehabilitation medicine

As the need for physical rehabilitation increases, novel treatment equipment and techniques have to be developed and tested. Refinement of these new methods needs changing various parameters and testing of the resultant techniques on individuals, which is time-consuming and costly. Development of musculoskeletal models enables computer simulation of movements to assess the effect of new modifications on the efficiency of training. Pei et al, 66 developed a robotic technique for physiotherapy of the lower limb. A GA was applied to generate custom-made treatment plans for each patient.

In another paper, a therapeutic robot was designed for lower limb exercise. The system that consisted of an ANN and a GA was capable of learning the actions of a physiotherapist for each patient and mimicked its behavior in the absence of a therapist. 67

Orthopedics

Biomedical engineering has offered great solutions to the field of orthopedic surgery. Total hip arthroplasty (THA) has improved the management of various disabling hip joint diseases. Yet, failure of the femoral stem of a THA can compromise the success of treatment. Ishida et al, 68 reported the use of a GA in designing an optimized geometry of the femoral stem component. GAs have also been exploited to select the best design of tibial locking screws to reduce the probability of screw breakage or loosening. 69 In another report, a combination of ANNs and GAs was applied to design spinal pedicle screws used for fixation of spinal fractures. The hybrid algorithm was able to design screws with a higher fatigue life and ideal pullout and bending characteristics. 70

Scoliosis is a three-dimensional deformity of spinal axis curves. The progression of the disease, which only happens in a small percentage of patients, is monitored by serial X-rays over time. Since frequent exposure to X-rays might increase the chance of cancer, it is desirable to assess the disease development using harmless methods. Jaremko et al, 71 developed a GA-based ANN algorithm to estimate the angle of spinal axis deformity from indices of trunk surface deformity. The hybrid system was able to determine the angle deformity within 5% accuracy in more than two third of patients.

Multiple sclerosis (MS) is a debilitating inflammatory disease of the neural system characterized by the formation of white matter scars otherwise known as plaques. Computer-assisted diagnosis has been applied for detection of pathologic features in these patients. In one study, a GA was developed to detect the MS lesions of brain MRIs. The similarity index of lesions determined by the GA and by a radiologist was 87%. 72

The EEG is a useful diagnostic method to detect the abnormal brain electrical discharges occurring during a seizure. To design an automated system for detection of abnormal EEG signals, several learning algorithms (LM, Quickprop, Delta-bar delta, and Momentum and Conjugate gradient) were used to train an ANN for EEG-based classification of epileptic versus healthy individuals. A GA was used to find the optimal parameters for and architecture of the ANN. The results demonstrated that the LM method combined with the GA was the best algorithm for training the ANN, which reached a general success of 96.5% in its performance. 73

Several reports have suggested that mitochondrial dysfunction plays an important role in Parkinson’s disease. Since mitochondrial genetics has its idiosyncrasies, a simple comparison of mitochondrial mutations between healthy and disease conditions might not be so informative. Therefore, Smigrodzki et al, 74 devised a GA to detect biologically important patterns of mitochondrial mutations in Parkinson’s patients. The proposed system was able to diagnose Parkinson’s disease with 100% accuracy based on mutational patterns in mitochondrial DNA.

Pharmacotherapy

Pharmacovigilance, the study of safety and adverse effects of drugs, is not only an integral part of currently-used drug assessment; it is also a crucial element in the evaluation of novel investigational medicines. The clinical judgment of a pharmacotherapist to attribute an observed adverse effect to a drug is valuable yet implicit while algorithms can make a less arbitrary and more objective evaluation. Koh et al, 75 developed a GA-based quantitative system for the evaluation of adverse drug reactions. The new scoring system was able to determine a probability of the causality of an adverse drug reaction to a suspected drug with about 84% sensitivity and 71% specificity.

Tacrolimus is an immunosuppressive agent used to prevent rejection after organ transplantation. The drug has highly variable pharmacokinetics and a narrow therapeutic window making its blood level control an essential and difficult task. In an attempt to predict the blood concentration of tacrolimus in liver-transplanted patients, an ANN algorithm was developed. A GA was used to choose the best set of clinically significant candidate variables. For validation, predicted results were compared to observed figures. The ANN was able to predict the blood level of tacrolimus, with 84% of data sets being within a clinically acceptable range of 3 ng/ml of the observed data. 76

Studies have shown that poor pharmacokinetics and lack of efficiency account for more than 50% of failures in the process of drug development. The traditional assessment of the efficacy and pharmacokinetics of novel investigational agents in animal models is a costly and time-consuming process. Therefore, computational methods have evolved to generate quantitative structure-pharmacokinetic relationship (QSPKR) models for rapid in silico screening of novel potential drugs.

Zandkarimi et al, 77 applied a GA to select the most suitable characteristics out of more than 1480 descriptors of alkaloid drugs. These sets of characteristics were then extracted from known drugs for training an ANN to generate QSPKR prediction models. The new system was able to predict the volume of distribution, clearance, and plasma protein binding of alkaloid drugs with an acceptable efficiency.

Health care management

Proper management of monetary resources and personnel is an integral part of health systems all over the world. One of the important elements of hospital management which can improve patient servicing, satisfaction, and cost-effectiveness ratios is efficient scheduling of patients admission. A mathematical model was developed and optimized using a GA to improve the patient scheduling in an ophthalmology hospital. The new algorithm was superior to the traditional "first come, first serve" model in that it shortened the waiting list, lowered the vacancy rate of hospital beds, reduceed the preoperative waiting time for patients, and increased the number of patients discharged from the hospital. 78 Another report showed that a combination of GA and particle swarm optimization, another powerful metaheuristic algorithm, was able to improve patient scheduling, reduce time wastage, and increase patient satisfaction. 79

In clinical laboratories, regular rotation of staff based on their skills through different facilities is fundamental for maintaining job skills and competence. GAs have been applied to improve staff rotation scheduling in a clinical laboratory. In one report, the GA-based software was capable of planning the rotation of staff effectively, ensuring maintenance of techniques and skills, saving time and the cost necessary for the scheduling process, and it was associated with the satisfaction of responsible supervisory personnel. 80

In this paper, we introduced GAs and some of their applications in various fields of medicine. Although GAs and some other metaheuristics are inspired by biology, the experts of other fields of science are more aware of them and these methods are frequently used to solve complex problems. Due to the inherent complexity of medicine, optimization methods could be of great value for physicians and medical researchers. The lack of an efficient interaction between computer scientists and physicians on the one hand and the unfamiliarity of complex mathematical formulas among the medical professions on the other is responsible for this situation. Therefore, improving the interaction and understanding between physicians, computer scientists, and engineers, which could happen via joint journal clubs or attendance of physicians ground rounds and case report presentations, could solve the problem. Besides, improvement of interdisciplinary courses and efficient involvement of engineering researchers in health care environments and hospitals could offer new solutions for medical problems and new ideas for non-medical researchers.

The authors declared no conflicts of interest. No funding was received for this study.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 27 August 2024

Artificial intelligence in medical genomics

• Yoichiro Kamatani   ORCID: orcid.org/0000-0001-8748-5597 1 &
• Tadashi Kaname   ORCID: orcid.org/0000-0003-0281-9610 2  

Journal of Human Genetics ( 2024 ) Cite this article

171 Accesses

1 Altmetric

Metrics details

• Medical genomics

Artificial intelligence (AI) and machine learning (ML) are rapidly growing and becoming essential research tools in the world of science. Genetics is no exception, and many approaches have been developed to incorporate a variety of these techniques. For example, Support Vector Machine (SVM) was used for genome-wide automatic variant filtering in the early phase of the 1000 genomes project, or for integrating prediction scores of the variant impacts, such as SIFT or PolyPhen-2, through CADD (Combined Annotation-Dependent Depletion).

The convolutional neural network, which revolutionized image learning and initially led the third wave of the AI boom, also forms the basis of several state-of-the-art genetic methods. These include DeepVariant for variant calling, SpliceAI for predicting the variant impact on splicing patterns, and DeepSEA, ExPecto, and Basenji for predicting the effect of non-coding genetic variations on gene expression levels.

Lastly, a language model with contextual understanding, based on Transformer architecture, realized highly accurate protein structure prediction by AlphaFold, followed by a better prediction of variant impacts with fine-tuning by human and primate frequency data, as seen by AlphaMissense. The Transformer-based language model was further utilized for predicting non-coding variant functions by Enformer. In addition, generative AI based on language models (e.g., large-scale language model) is being used to support diagnosis of rare diseases.

As shown by these examples, the application of AI techniques in genetics is one of the highly productive research fields that has explored many new possibilities. It is also worth noting that AI has significant applications in cancer genomics as well. In this special issue of the Journal of Human Genetics , we aim to introduce various possibilities to our readers by having the developers of AI methods explain the diverse applications.

Author information

Authors and affiliations.

Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

Yoichiro Kamatani

Department of Genome Medicine, National Center for Child Health and Development, Tokyo, Japan

Tadashi Kaname

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Yoichiro Kamatani .

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Kamatani, Y., Kaname, T. Artificial intelligence in medical genomics. J Hum Genet (2024). https://doi.org/10.1038/s10038-024-01282-1

Download citation

Received : 29 July 2024

Accepted : 29 July 2024

Published : 27 August 2024

DOI : https://doi.org/10.1038/s10038-024-01282-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

A Study on Genetic Algorithm and its Applications

• October 2016
• International Journal of Computer Sciences and Engineering 4(10):139-143
• 4(10):139-143

Abstract and Figures

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations
• Salvatore Prestipino
• Andrea Rapisarda

• W. Piserchia
• H. Abubakar

• Dongwon Lee

• Yourim Yoon
• Wanjun Duan
• Haiquan Zhao
• Yangyang Liang
• Malika Sader
• Feng-Yao Lyu
• Zhen-Fei Zhan
• Gui-Lin Zhou
• Ningchao Wu
• Xingchuan Bao
• Dayang Wang
• Navjot Singh
• Jagpreet Singh
• Glykeria Kyrou

• Alaa H. Aly

• Bahgat A. Abdel Latef

• J.H. Holland
• Animesh Shrivastava
• Singh Rajawat
• Chaitali R Gaidhani
• Vedashree M Deshpande
• Vrushali N Bora
• J COMPUT-MEDIAT COMM

• INT J INNOV COMPUT I
• Ching-San Chiang

• Yi-Chih Hsin
• Ming-Hui Wang
• John H Holland
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Genetic optimization of twin-web turbine disc cavities in aeroengines.

1. Introduction

2. calculation process, 2.1. model and boundary conditions, 2.2. grid and independence, 2.3. evaluation indicators, 3. design parameters and methods, 3.1. design parameters, 3.2. bp neural network, 3.3. genetic algorithm, 4. results and analysis, 4.1. comparison of t max, 4.2. comparison of other evaluation indicators, 4.2.1. flow field distribution, 4.2.2. temperature non-uniformity coefficient, 4.2.3. wall nusselt number, 5. conclusions.

• The surrogate model established by the neural network and the genetic algorithm can effectively solve the objective function and realize the optimal design of the TWD. The results of the GA-BP surrogate model developed in this study closely match those obtained through the finite algorithm.
• Optimizing the tangential angles of the nozzles and receiver holes significantly impacts the flow of coolant in the cavity, improving heat transfer in high-radius areas, reducing areas with poor heat transfer performance like standing vortices, and enhancing the cooling efficiency of the TWD. The maximum temperature T max of the TWD of the optimized model decreases by 23.1 K and 32.4 K on average under different working conditions, which has better performance than the basic model under various working conditions.
• The temperature at the edge outlet of the optimized model is approximately 50 Kelvin lower than that of the basic model. Furthermore, the axial temperature consistency and radial temperature distribution of the TWD show significant enhancement. The radial temperature non-uniformity coefficient T vr decreased dramatically, with an average reduction of 11.56% and a maximum reduction of 20.87%. The axial temperature gradient fell, the average temperature gradient decreased by 53.62%, and the axial temperature non-uniformity coefficient also decreased by 27.24%.
• Compared with the basic model, the Nusselt number of the optimized model has been considerably improved. The trend in the Nusselt number in the rotor-stator cavities remains constant but the value significantly increases. The optimization of the Nusselt number in the inner cavity is particularly noticeable, especially in regions with low heat transfer.

Supplementary Materials

Author contributions, data availability statement, conflicts of interest.

• Zhang, F.; Wang, X.; Li, J. Numerical investigation of flow and heat transfer characteristics in radial pre-swirl system with different pre-swirl nozzle angles. Int. J. Heat Mass Transf. 2016 , 95 , 984–995. [ Google Scholar ] [ CrossRef ]
• Zhang, F.; Wang, X.; Li, J. Numerical investigation on the flow and heat transfer characteristics in radial pre-swirl system with different fillet radius at the junction of inlet cavity and nozzle. Appl. Therm. Eng. 2016 , 106 , 1165–1175. [ Google Scholar ] [ CrossRef ]
• Liu, G.-W.; Zhang, L.; Wu, W.-T.; Feng, Q. Numerical simulations on the flow characteristics of the pre-swirl nozzles with different length-to-diameter ratios. J. Propuls. Technol. 2013 , 34 , 644–650. [ Google Scholar ]
• Ciampoli, F.; Chew, J.W.; Shahpar, S.; Willocq, E. Automatic Optimization of Preswirl Nozzle Design. ASME J. Eng. Gas Turbines Power 2007 , 129 , 387–393. [ Google Scholar ] [ CrossRef ]
• Lewis, P.; Wilson, M.; Lock, G.; Owen, J.M. Effect of Radial Location of Nozzles on Performance of Pre-Swirl Systems. In Proceedings of the ASME Turbo Expo 2008: Power for Land, Sea, and Air, Berlin, Germany, 9–13 June 2008; Volume 4: Heat Transfer, Parts A and B. ASME: New York, NY, USA, 2008; pp. 1397–1406. [ Google Scholar ]
• Kakade, V.U.; Lock, G.D.; Wilson, M.; Owen, J.M.; Mayhew, J.E. Effect of radial location of nozzles on heat transfer in pre-swirl cooling systems. In Proceedings of the ASME Turbo Expo 2009, Orlando, FL, USA, 8–12 June 2009; ASME: New York, NY, USA, 2009; Volume 3, pp. 1051–1060. [ Google Scholar ]
• Kong, X.; Huang, T.; Liu, Y.; Chen, H.; Lu, H. Effects of pre-swirl radius on cooling performance of a rotor-stator pre-swirl system in gas turbine engines. Case Stud. Therm. Eng. 2022 , 37 , 102250. [ Google Scholar ] [ CrossRef ]
• Ennacer, M.; Guevremont, G.; Djeridane, T.; Sreekanth, S.; Lucas, T. Blade Air Cooling Feed System CFD Analysis and Validation. In Proceedings of the ASME Turbo Expo 2007: Power for Land, Sea, and Air, Montreal, QC, Canada, 14–17 May 2007; Volume 6: Turbo Expo 2007, Parts A and B. ASME: New York, NY, USA, 2007; pp. 1171–1180. [ Google Scholar ]
• Sousek, J.; Pfitzner, M.; Niehuis, R. Experimental Study of Discharge Coefficients for Radial Orifices in High-Speed Rotating Shafts. In Proceedings of the ASME Turbo Expo 2010: Power for Land, Sea, and Air, Glasgow, UK, 14–18 June 2010; Volume 4: Heat Transfer, Parts A and B. ASMEDC: Glasgow, UK, 2010; pp. 1051–1060. [ Google Scholar ]
• Ballal, D.; Zelina, J. Progress in Aero-Engine Technology, 1939–2003 (invited). In Proceedings of the 39th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, Huntsville, AL, USA, 20–23 July 2003; American Institute of Aeronautics and Astronautics: Huntsville, AL, USA, 2003. [ Google Scholar ]
• Cairo, R.R. Twin–Web Rotor Disk. U.S. Patent 5961287[P], 5 October 1999. [ Google Scholar ]
• Cairo, R.R.; Sargent, K.A. Twin web disk: A step beyond convention. J. Eng. Gas Turbines Power 2002 , 124 , 298–302. [ Google Scholar ] [ CrossRef ]
• Vasilyev, B.; Salnikov, A.; Semenov, A.; Magerramova, L. Twin-Web Turbine Discs: Part 1—Design and Analysis of Their Efficiency. In Proceedings of the ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition, Oslo, Norway, 11–15 June 2018; Volume 7A: Structures and Dynamics, V07AT30A006. ASME: New York, NY, USA, 2018. [ Google Scholar ]
• Wang, B.; Wang, G.; Huang, L.; Xu, S. On the preliminary shape design of axisymmetric twin-web turbine discs considering the burst speed constraint. Eng. Optim. 2022 , 54 , 2071–2086. [ Google Scholar ] [ CrossRef ]
• Li, L.; Tang, Z.; Li, H.; Gao, W.; Yue, Z.; Xie, G. Convective heat transfer characteristics of twin-web turbine disk with pin fins in the inner cavity. Int. J. Therm. Sci. 2020 , 152 , 106303. [ Google Scholar ] [ CrossRef ]
• Zhang, M.; Gou, W.; Yao, Q.; Li, L.; Yue, Z. Investigation on heat transfer characteristic and optimization of the cooling air inlet for the twin-web turbine disk. J. Phys. Conf. Ser. 2017 , 885 , 012011. [ Google Scholar ] [ CrossRef ]
• Zhang, M.; Gou, W.; Li, L.; Yang, F.; Yue, Z. Multidisciplinary design and multi-objective optimization on guide fins of twin-web disk using Kriging surrogate model. Struct. Multidiscip. Optim. 2017 , 55 , 361–373. [ Google Scholar ] [ CrossRef ]
• Zhang, M.; Gou, W.; Li, L.; Wang, X.; Yue, Z. Multidisciplinary design and optimization of the twin-web turbine disk. Struct. Multidiscip. Optim. 2016 , 53 , 1129–1141. [ Google Scholar ] [ CrossRef ]
• Ma, A.; Liu, F.; Zhou, T.; Hu, R. Numerical investigation on heat transfer characteristics of twin-web turbine disk-cavity system. Appl. Therm. Eng. 2020 , 184 , 116268. [ Google Scholar ] [ CrossRef ]
• Ma, A.; Wu, Q.; Zhou, T.; Hu, R. Effect of Inlet Flow Ratio on Heat Transfer Characteristics of a Novel Twin-Web Turbine Disk with Receiving Holes. SSRN Electron. J. 2022 , 34 , 101990. [ Google Scholar ]
• Brunton, S.; Noack, B.; Koumoutsakos, P. Machine Learning for Fluid Mechanics. Annu. Rev. Fluid Mech. 2020 , 52 , 477–508. [ Google Scholar ] [ CrossRef ]
• Brunton, S.L. Applying machine learning to study fluid mechanics. Acta Mech. Sin. 2021 , 37 , 1718–1726. [ Google Scholar ] [ CrossRef ]
• Cai, S.; Mao, Z.; Wang, Z.; Yin, M.; Karniadakis, G.E. Physics-informed neural networks (PINNs) for fluid mechanics: A review. Acta Mech. Sin. 2021 , 37 , 1727–1738. [ Google Scholar ] [ CrossRef ]
• Zuhal, L.R.; Palar, P.S.; Shimoyama, K. A comparative study of multi-objective expected improvement for aerodynamic design. Aerosp. Sci. Technol. 2019 , 91 , 548–560. [ Google Scholar ] [ CrossRef ]
• Jilin, L.; Shunwen, X.; Yi, L.; Xiwen, D.; Ao, T.; Lin, D. Multi-objective optimisation of heat transfer and structural strength of aero-piston air-cooled engine cylinder based on orthogonal test. Therm. Sci. Eng. Prog. 2024 , 50 , 102500. [ Google Scholar ] [ CrossRef ]
• Zhang, M.; Liu, D.; Liu, Y. Recent progress in precision measurement and assembly optimization methods of the aero-engine multistage rotor: A comprehensive review. Measurement 2024 , 235 , 114990. [ Google Scholar ] [ CrossRef ]
• Jia, X.; Zhou, D.; Hao, J.; Ma, Y.; Peng, Z. Dynamic simulation based on feature transfer learning with source domain adaptive optimization: Application of data-driven model for aero-engines. Measurement 2023 , 223 , 113786. [ Google Scholar ] [ CrossRef ]
• Li, B.; Gu, C.; Li, X.; Liu, T. Numerical optimization for stator vane settings of multi-stage compressors based on neural networks and genetic algorithms. Aerosp. Sci. Technol. 2016 , 52 , 81–94. [ Google Scholar ] [ CrossRef ]
• Xiang, S.; Qin, Y.; Luo, J.; Pu, H.; Tang, B. Multicellular LSTM-based deep learning model for aero-engine remaining useful life prediction. Reliab. Eng. Syst. Saf. 2021 , 216 , 107927. [ Google Scholar ] [ CrossRef ]
• Aygun, H.; Turan, O. Application of genetic algorithm in exergy and sustainability: A case of aero-gas turbine engine at cruise phase. Energy 2022 , 238 , 121644. [ Google Scholar ] [ CrossRef ]
• Liu, P.; Han, H.C.; Bao, Z. Multi-objective optimization of fuel–air tube-in-tube helical coil heat exchangers for cooled cooling air system applied in aeroengines. Aerosp. Sci. Technol. 2022 , 130 , 107933. [ Google Scholar ] [ CrossRef ]
• Zhang, C.; Gümmer, V. Multi-objective optimization and system evaluation of recuperated helicopter turboshaft engines. Energy 2020 , 191 , 116477. [ Google Scholar ] [ CrossRef ]
• Xu, G.Q.; Zhang, S.; Luo, X.; Ding, S.T.; Tao, Z.; Lei, B. Experimental investigation on heat transfer in shrouded rotating disk with high-positioned air inflow. J. Aerosp. Power 2006 , 21 , 820–823. (In Chinese) [ Google Scholar ]
• Forrester, A.; Sobester, A.; Keane, A. Engineering Design via Surrogate Modelling: A Practical Guide ; John Wiley & Sons: Hoboken, NJ, USA, 2008. [ Google Scholar ]
• Gong, W.; Liu, G.; Wang, J.; Wang, F.; Lin, A.; Wang, Z. Aerodynamic and thermodynamic analysis of an aero-engine pre-swirl system based on structure design and performance improvement. Aerosp. Sci. Technol. 2022 , 123 , 107466. [ Google Scholar ] [ CrossRef ]
• Hide, R. On source-sink flows in a rotating fluid. J. Fluid Mech. 1968 , 32 , 737–764. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Guo, Y.; Wang, S.; Shen, W. Genetic Optimization of Twin-Web Turbine Disc Cavities in Aeroengines. Energies 2024 , 17 , 4346. https://doi.org/10.3390/en17174346

Guo Y, Wang S, Shen W. Genetic Optimization of Twin-Web Turbine Disc Cavities in Aeroengines. Energies . 2024; 17(17):4346. https://doi.org/10.3390/en17174346

Guo, Yueteng, Suofang Wang, and Wenjie Shen. 2024. "Genetic Optimization of Twin-Web Turbine Disc Cavities in Aeroengines" Energies 17, no. 17: 4346. https://doi.org/10.3390/en17174346

Article Metrics

Article access statistics, supplementary material.

ZIP-Document (ZIP, 9 KiB)

Further Information

Mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

• DOI: 10.3390/ma17164077
• Corpus ID: 272033185

Optimized Design of Low-Carbon Mix Ratio for Non-Dominated Sorting Genetic Algorithm II Concrete Based on Genetic Algorithm-Improved Back Propagation

• Fan Zhang , Bo Wen , +2 authors Bingbing Guo
• Published in Materials 1 August 2024
• Engineering, Materials Science

Figures and Tables from this paper

47 References

Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: a review, development of high performance geopolymer concrete with waste rubber and recycle steel fiber: a study on compressive behavior, carbon emissions and economical performance, prediction of the corrosion depth of oil well cement corroded by carbon dioxide using ga-bp neural network, prediction of high-performance concrete compressive strength using deep learning techniques, optimization of high-performance concrete mix ratio design using machine learning, a multi-objective medical process mining model using event log and casual matrix, machine learning-based predictive modeling of sustainable lightweight aggregate concrete, using ga - bp coupling algorithm to predict the high-performance concrete mechanical property, mixture optimization of cementitious materials using machine learning and metaheuristic algorithms: state of the art and future prospects, effect of recycled concrete aggregate features on adhesion properties of asphalt mortar-aggregate interface, related papers.

Showing 1 through 3 of 0 Related Papers

IEEE Account

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Towards a Smart Intelligent Fuzzy System in Assessing Traffic Crash Risks Among Female Teen Drivers: A Genetic Algorithm Approach Using Driving Simulator Research

• Conference paper
• First Online: 30 August 2024
• Cite this conference paper

• Soukaina EL Ferouali 15 ,
• Zouhair Elamrani Abou Elassad 16 , 17 &
• Abdelmounaîm Abdali 15  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1089))

Included in the following conference series:

• International Conference on Intelligent and Fuzzy Systems

It is noted that technological advancements such as Artificial Intelligence (AI), Machine Learning, Big Data, Internet of Things (IoT), Geographic Information Systems (GIS), Global Positioning System (GPS), Simulators, and others can provide practical methods for recognizing and providing details on variables impacting road safety, such as user behavior, physical road characteristics, and accident severity. To further traffic safety investigations, this study uses a driving simulator and feature selection based on genetic algorithms(GAs) to examine road accidents among female teenage drivers which continue to be represented in crash fatalities and injuries, particularly in nighttime and rainy weather circumstances. The objective is to find the best feature combinations that affect the probability of an accident by integrating fitness functions that are represented by predictive models. While navigating the feature space, the genetic algorithm chooses combinations that maximize prediction accuracy. The study’s results identified pivotal features crucial for road safety among female teen drivers. Selected attributes encompassed an array of variables including steering, RPM, Speed, Ambient temperature, and Weather conditions which represent driving dynamics, vehicle performance, environmental conditions, and meteorological influences. Little studies have been done into the effects of weather conditions on young female drivers at night. GAs perform better, which is why we can use them in this particular situation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

World Health Organization: Global Status Report on Road Safety 2023. World Health Organization, Geneva, Switzerland (2023)

Google Scholar  

El Ferouali, S., Elamrani Abou Elassad, Z., Abdali, A.: Understanding the factors contributing to traffic accidents: survey and taxonomy. In: Farhaoui, Y., Hussain, A., Saba, T., Taherdoost, H., Verma, A. (eds.) Artificial Intelligence, Data Science and Applications. ICAISE 2023. Lecture Notes in Networks and Systems, vol. 838, pp. 214–221. Springer, Cham (2024). https://doi.org/10.1007/978-3-031-48573-2_31

Scott-Parker, B., Hyde, M., Watson, B., King, M.: Speeding by young novice drivers: what can personal characteristics and psychosocial theory add to our understanding? Accid. Anal. Prev. 50 , 242–250 (2013)

Article   Google Scholar  

Engstr om, I., Gregersen, N.P., Hernetkoski, K., Keskinen, E., Nyberg, A.: Young novice drivers, driver education and training: Literature review. VTI rapport 491A (2003)

Gruber, N., Mosimann, U.P., Muri, R.M., Nef, T.: Vision and night driving abilities of elderly drivers. Traffic Inj. Prev. 14 , 477–485 (2013)

Plainis, S., Murray, I.: Reaction times as an index of visual conspicuity when driving at night. Ophthalmic Physiol. Opt. 22 , 409–15 (2002)

Elamrani Abou Elassad, Z., Mousannif, H., Al Moatassime, H., Karkouch, A.: The application of machine learning techniques for driving behavior analysis: a conceptual framework and a systematic literature review. Eng. Appl. Artif. Intell. 87 , 103312 (2020)

Mafi, S., AbdelRazig, Y., Doczy, R.: Machine learning methods to analyze injury severity of drivers from different age and gender groups. Transp. Res. Rec. 2672 (38), 171–183 (2018)

Kim, J.-K., Ulfarsson, G., Kim, S., Shankar, V.: Driver-injury severity in single- vehicle crashes in California: a mixed logit analysis of heterogeneity due to age and gender. Accid. Anal. Prev. 50 , 1073–1081 (2012)

Keall, M., Frith, W.: Older driver crash rates in relation to type and quantity of travel. Traffic Inj. Prev. 5 , 26–36 (2004)

Regev, S., Rolison, J., Moutari, S.: Crash risk by driver age, gender, and time of day using a new exposure methodology. J. Saf. Res. 66 , 131–140 (2018)

Elamrani Abou Elassad, Z., Mousannif, H.: Understanding driving behavior: measurement, modeling and analysis. In: Ezziyyani, M. (ed.) AI2SD 2018. AISC, vol. 915, pp. 452–464. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11928-7_41

Chapter   Google Scholar  

Sivanandam, S., Deepa, S., Sivanandam, S., Deepa, S.: Genetic Algorithms, pp. 15–16. Springer (2008). https://doi.org/10.1007/978-3-540-73190-0

Babatunde, O.H., Armstrong, L., Leng, J., Diepeveen, D.: A genetic algorithm- based feature selection. IJECCE 5 (4), 889–905 (2014)

El Ferouali, S., Elamrani Abou Elassad, Z., Abdali, A.: Does gender affect the identification of road crash occurrences? an overview and a comparative study. Int. J. Comput. Eng. Data Sci. (IJCEDS) 3 (1), 8–12 (2023)

Taherdangkoo, M., Paziresh, M., Yazdi, M., Bagheri, M.H.: An efficient algorithm for function optimization: modified stem cells algorithm. Cent. Eur. J. Eng. 3 , 36–50 (2012)

Zhang, J., Chung, H., Lo, W.L.: Clustering-based adaptive crossover and mutation probabilities for genetic algorithm. IEEE Trans. Evol. Comput. 11 , 326–335 (2007)

Akbari, R., Ziarati, K.: A multilevel evolutionary algorithm for optimizing numerical functions. Int. J. Ind. Eng. Comput. 2 , 419–430 (2011)

Download references

Author information

Authors and affiliations.

CISIEV Team, IT Department, Faculty of Science and Technology, Cadi Ayyad University, Marrakech, Morocco

Soukaina EL Ferouali & Abdelmounaîm Abdali

SARS Research Team, Computer Science Department, ENSAS, Cadi Ayyad University, Safi, Morocco

Zouhair Elamrani Abou Elassad

Data Science Laboratory, Information Technology Management Department, Daffodil International University, Birulia, Bangladesh

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Soukaina EL Ferouali .

Editor information

Editors and affiliations.

Department of Industrial Engineering, Istanbul Technical University, Istanbul, Türkiye

Cengiz Kahraman

Sezi Cevik Onar

Department of Industrial Engineering, Yildiz Technical University, Besiktas, Istanbul, Türkiye

Selcuk Cebi

Department of Industrial Engineering, Istanbul Technical University, Besiktas, Istanbul, Türkiye

Basar Oztaysi

Department of Industrial Engineering, Galatasaray University, Istanbul, Türkiye

A. Cagrı Tolga

Irem Ucal Sari

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Cite this paper.

EL Ferouali, S., Elamrani Abou Elassad, Z., Abdali, A. (2024). Towards a Smart Intelligent Fuzzy System in Assessing Traffic Crash Risks Among Female Teen Drivers: A Genetic Algorithm Approach Using Driving Simulator Research. In: Kahraman, C., Cevik Onar, S., Cebi, S., Oztaysi, B., Tolga, A.C., Ucal Sari, I. (eds) Intelligent and Fuzzy Systems. INFUS 2024. Lecture Notes in Networks and Systems, vol 1089. Springer, Cham. https://doi.org/10.1007/978-3-031-67195-1_67

Download citation

DOI : https://doi.org/10.1007/978-3-031-67195-1_67

Published : 30 August 2024

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-67194-4

Online ISBN : 978-3-031-67195-1

eBook Packages : Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research