Artigos de revistas: "Genetic algorithms"

1

Sumida, Brian. "Genetics for genetic algorithms." ACM SIGBIO Newsletter 12, no. 2 (June 1992): 44–46. http://dx.doi.org/10.1145/130686.130694.

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Raol, Jitendra R., and Abhijit Jalisatgi. "From genetics to genetic algorithms." Resonance 1, no. 8 (August 1996): 43–54. http://dx.doi.org/10.1007/bf02837022.

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Nico, Nico, Novrido Charibaldi, and Yuli Fauziah. "Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center." International Journal of Artificial Intelligence & Robotics (IJAIR) 4, no. 1 (May 30, 2022): 9–23. http://dx.doi.org/10.25139/ijair.v4i1.4323.

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 One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorithms among evolutionary algorithms for scheduling are genetic algorithms and memetics algorithms. When it comes to the two algorithms, using genetic algorithms or memetics algorithms may not always offer the optimum outcomes in every situation. Therefore, it is necessary to compare the genetic algorithm and the algorithm's memetic algorithm to determine which one is suitable for the nurse picket schedule. From the results of this study, the memetic algorithm is better than the genetic algorithm in making picket schedules. The memetic algorithm with a population of 10000 and a generation of 5000 does not produce convergent data. While for the genetic algorithm, when the population is 5000 and the generation is 50, the data convergence starts. For accuracy, the memetic algorithm violates only 24 of the 124 existing constraints (80,645%). The genetic algorithm violates 27 of the 124 constraints (78,225%). The average runtime used to generate optimal data using the memetic algorithm takes 20.935592 seconds. For the genetic algorithm, it takes longer, as much as 53.951508 seconds.

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Babu, M. Nishidhar, Y. Kiran, and A. Ramesh V. Rajendra. "Tackling Real-Coded Genetic Algorithms." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 217–23. http://dx.doi.org/10.31142/ijtsrd5905.

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Srinath Murthy, Ahana, and Dattatreya P Mankame. "Genetic Algorithms - A Brief Study." International Journal of Science and Research (IJSR) 13, no. 7 (July 5, 2024): 1195–200. http://dx.doi.org/10.21275/sr24721004409.

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Abbas, Basim K. "Genetic Algorithms for Quadratic Equations." Aug-Sept 2023, no. 35 (August 26, 2023): 36–42. http://dx.doi.org/10.55529/jecnam.35.36.42.

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A common technique for finding accurate solutions to quadratic equations is to employ genetic algorithms. The authors propose using a genetic algorithm to find the complex roots of a quadratic problem. The technique begins by generating a collection of viable solutions, then proceeds to assess the suitability of each solution, choose parents for the next generation, and apply crossover and mutation to the offspring. For a predetermined number of generations, the process is repeated. Comparing the evolutionary algorithm's output to the quadratic formula proves its validity and uniqueness. Furthermore, the utility of the evolutionary algorithm has been demonstrated by programming it in Python code and comparing the outcomes to conventional intuitions.

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Carnahan, J., and R. Sinha. "Nature's algorithms [genetic algorithms]." IEEE Potentials 20, no. 2 (2001): 21–24. http://dx.doi.org/10.1109/45.954644.

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Rizky Fatih Syahputra and Yahfizham Yahfizham. "Menganalisis Konsep Dasar Algoritma Genetika." Bhinneka: Jurnal Bintang Pendidikan dan Bahasa 2, no. 1 (December 23, 2023): 120–32. http://dx.doi.org/10.59024/bhinneka.v2i1.643.

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Genetic algorithms are computer techniques inspired by the theory of evolution and genetics. Individual definition, chromosome initialization, chromosome testing, selection (crossover) and mutation are fundamental elements of genetic algorithms. Genetic algorithms are used to solve optimization problems, such as lesson planning, community services and traffic light adjustment. By producing the best combination of chromosomes, the genetic algorithm can achieve ideal results. The genetic algorithm produces appropriate planning data to avoid delays. This research uses the methods of data collection, individual definition and chromosome initialization. The result of this research is a service application designed to be able to plan efficiently through development using a genetic algorithm. Optimal planning occurs when processing planning data produces solutions that are efficient in terms of time, energy, and resources, and avoids conflicting schedules in the same place..

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M., Nishidhar Babu, Kiran Y., and Ramesh |. V. Rajendra A. "Tackling Real Coded Genetic Algorithms." International Journal of Trend in Scientific Research and Development 2, no. 1 (December 1, 2017): 217–23. https://doi.org/10.31142/ijtsrd5905.

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Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations selection, crossover and mutation.Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behavior of this type of genetic algorithms are revised and compared. M. Nishidhar Babu | Y. Kiran | A. Ramesh | V. Rajendra "Tackling Real-Coded Genetic Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: https://www.ijtsrd.com/papers/ijtsrd5905.pdf

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Kim, Yong-Hyuk, Zong Woo Geem, and Yourim Yoon. "Population-Based Redundancy Control in Genetic Algorithms: Enhancing Max-Cut Optimization." Mathematics 13, no. 9 (April 25, 2025): 1409. https://doi.org/10.3390/math13091409.

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The max-cut problem is a well-known topic in combinatorial optimization, with a wide range of practical applications. Given its NP-hard nature, heuristic approaches—such as genetic algorithms, tabu search, and harmony search—have been extensively employed. Recent research has demonstrated that harmony search can outperform genetic algorithms by effectively avoiding redundant searches, a strategy similar to tabu search. In this study, we propose a modified genetic algorithm that integrates tabu search to enhance solution quality. By preventing repeated exploration of previously visited solutions, the proposed method significantly improves the efficiency of traditional genetic algorithms and achieves performance levels comparable to harmony search. The experimental results confirm that the proposed algorithm outperforms standard genetic algorithms on the max-cut problem. This work demonstrates the effectiveness of combining tabu search with genetic algorithms and offers valuable insights into the enhancement of heuristic optimization techniques. The novelty of our approach lies in integrating solution-level tabu constraints directly into the genetic algorithm’s population dynamics, enabling redundancy prevention without additional memory overhead, a strategy not previously explored in the proposed hybrids.

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EZZIANE, ZOHEIR. "Solving the 0/1 knapsack problem using an adaptive genetic algorithm." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 1 (January 2002): 23–30. http://dx.doi.org/10.1017/s0890060401020030.

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Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

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12

Burke, Donald S., Kenneth A. De Jong, John J. Grefenstette, Connie Loggia Ramsey, and Annie S. Wu. "Putting More Genetics into Genetic Algorithms." Evolutionary Computation 6, no. 4 (December 1998): 387–410. http://dx.doi.org/10.1162/evco.1998.6.4.387.

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The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an interdisciplinary project whose goal is to build GA-based computational models of viral evolution. The result is a system called Virtual Virus (VIV). VIV incorporates a number of more biologically plausible mechanisms, including a more flexible genotype-to-phenotype mapping. In VIV the genes are independent of position, and genomes can vary in length and may contain noncoding regions, as well as duplicative or competing genes. Initial computational studies with VIV have already revealed several emergent phenomena of both biological and computational interest. In the absence of any penalty based on genome length, VIV develops individuals with long genomes and also performs more poorly (from a problem-solving viewpoint) than when a length penalty is used. With a fixed linear length penalty, genome length tends to increase dramatically in the early phases of evolution and then decrease to a level based on the mutation rate. The plateau genome length (i.e., the average length of individuals in the final population) generally increases in response to an increase in the base mutation rate. When VIV converges, there tend to be many copies of good alternative genes within the individuals. We observed many instances of switching between active and inactive genes during the entire evolutionary process. These observations support the conclusion that noncoding regions serve as scratch space in which VIV can explore alternative gene values. These results represent a positive step in understanding how GAs might exploit more of the power and flexibility of biological evolution while simultaneously providing better tools for understanding evolving biological systems.

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13

Megson, G. M., and I. M. Bland. "Generic systolic array for genetic algorithms." IEE Proceedings - Computers and Digital Techniques 144, no. 2 (1997): 107. http://dx.doi.org/10.1049/ip-cdt:19971126.

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14

Frenzel, J. F. "Genetic algorithms." IEEE Potentials 12, no. 3 (October 1993): 21–24. http://dx.doi.org/10.1109/45.282292.

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15

Fulkerson, William F. "Genetic Algorithms." Journal of the American Statistical Association 97, no. 457 (March 2002): 366. http://dx.doi.org/10.1198/jasa.2002.s468.

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16

Forrest, Stephanie. "Genetic algorithms." ACM Computing Surveys 28, no. 1 (March 1996): 77–80. http://dx.doi.org/10.1145/234313.234350.

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17

Holland, John H. "Genetic Algorithms." Scientific American 267, no. 1 (July 1992): 66–72. http://dx.doi.org/10.1038/scientificamerican0792-66.

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18

Coley, D. A. "Genetic algorithms." Contemporary Physics 37, no. 2 (March 1996): 145–54. http://dx.doi.org/10.1080/00107519608230341.

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19

Grupe, Fritz H., and Simon Jooste. "Genetic algorithms." Information Management & Computer Security 12, no. 3 (July 2004): 288–97. http://dx.doi.org/10.1108/09685220410542624.

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20

Skomorokhov, Alexander O. "Genetic algorithms." ACM SIGAPL APL Quote Quad 26, no. 4 (June 15, 1996): 97–106. http://dx.doi.org/10.1145/253417.253399.

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21

Holland, John. "Genetic algorithms." Scholarpedia 7, no. 12 (2012): 1482. http://dx.doi.org/10.4249/scholarpedia.1482.

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22

Alfonseca, Manuel. "Genetic algorithms." ACM SIGAPL APL Quote Quad 21, no. 4 (July 1991): 1–6. http://dx.doi.org/10.1145/114055.114056.

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23

Turčaník, Michal, and Martin Javurek. "Cryptographic Key Generation by Genetic Algorithms." Information & Security: An International Journal 43, no. 1 (2019): 54–61. http://dx.doi.org/10.11610/isij.4305.

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24

Agapie, Alexandru. "Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms." Evolutionary Computation 9, no. 2 (June 2001): 127–46. http://dx.doi.org/10.1162/106365601750190370.

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Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting for a complete, rather than recent, history of the algorithm's evolution. Under the new paradigm, we analyze the convergence of several mutation-adaptive algorithms: a binary genetic algorithm, the 1/5 success rule evolution strategy, a continuous, respectively a dynamic (1+1) evolutionary algorithm.

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25

Wei, Wei, Liang Liu, Zhong Qin Hu, and Yu Jing Zhou. "Rigid Medical Image Registration Based on Genetic Algorithms and Mutual Information." Applied Mechanics and Materials 665 (October 2014): 712–17. http://dx.doi.org/10.4028/www.scientific.net/amm.665.712.

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With the variety of medical imaging equipment’s application in the medical process,medical image registration becomes particularly important in the field of medical image processing,which has important clinical diagnostic and therapeutic value. This article describes the matrix conversion method of the rigid registration model, the basic concepts and principles of the mutual information algorithm ,the basic idea of genetic algorithms and algorithm’s flow , and the application of the improved genetic algorithms in practice. The rigid registration of two CT brain bones images uses mutual information as a similarity measure, genetic algorithm as the search strategy and matlab as programming environment. Using the three-point crossover technique to exchange the three parameters in the rigid transformation repeectively to produce new individuals, the genetic algorithm’s local search ability enhanced and the prematurity phenomenon can be reduced through the depth study of the basic genetic algorithm. The experiments show that the registration has high stability and accuracy.

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Kanwal, Maxinder S., Avinash S. Ramesh, and Lauren A. Huang. "A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms." F1000Research 2 (November 19, 2013): 139. http://dx.doi.org/10.12688/f1000research.2-139.v2.

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Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

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D., KARDASH, and KOLLAROV O. "Solving optimization problems in energy with genetic algorithm." Journal of Electrical and power engineering 28, no. 1 (June 29, 2023): 37–41. http://dx.doi.org/10.31474/2074-2630-2023-1-37-41.

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The article discusses the application of genetic algorithms in the field of energy optimization. Linear programming is commonly used for optimization problems in energy systems. Linear programming is a mathematical optimization method that seeks the optimal solution under constraints, where all constraints and the objective function are linear functions. In the realm of artificial intelligence,genetic algorithms are employed for optimization tasks. genetic algorithms mimic natural evolution processes, including selection, crossover, mutation, and adaptation, to solve optimization and search problems. The article outlines the process of a genetic algorithm, starting with the formation of an initial population and proceeding through selection, crossover, mutation, and evaluation. This cycle repeats until an optimal solution is achieved. Advantages of genetic algorithms include their ability to handle complex solution spaces, find global optima, adapt to changing conditions, optimize multi-objective functions, and work with non-linear and non-differentiable objective functions. However, they may require significant computational resources and parameter tuning. The article then presents a case study of applying a genetic algorithm to optimize the allocation of a power load in an energy system. The mathematical model is developed, and the simplex method is initially used for solution. Subsequently, a Python program for genetic algorithm implementation is provided. The algorithm's efficiency and convergence are demonstrated through a graphical representation of the optimization process. In conclusion, the article highlights the effectiveness of genetic algorithms in energy optimization, showcasing their rapid convergence and ability to find near-optimal solutions in complex scenarios.

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Chapman, C. D., K. Saitou, and M. J. Jakiela. "Genetic Algorithms as an Approach to Configuration and Topology Design." Journal of Mechanical Design 116, no. 4 (December 1, 1994): 1005–12. http://dx.doi.org/10.1115/1.2919480.

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The genetic algorithm, a search and optimization technique based on the theory of natural selection, is applied to problems of structural topology design. An overview of the genetic algorithm will first describe the genetics-based representations and operators used in a typical genetic algorithm search. Then, a review of previous research in structural optimization is provided. A discretized design representation, and methods for mapping genetic algorithm “chromosomes” into this representation, is then detailed. Several examples of genetic algorithm-based structural topology optimization are provided: we address the optimization of cantilevered plate topologies, and we investigate methods for optimizing finely-discretized design domains. The genetic algorithm’s ability to find families of highly-fit designs is also examined. Finally, a description of potential future work in genetic algorithm-based structural topology optimization is offered.

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Chernov, Ivan E., and Andrey V. Kurov. "APPLICATION OF GENETIC ALGORITHMS IN CRYPTOGRAPHY." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 1 (2022): 63–82. http://dx.doi.org/10.28995/2686-679x-2022-1-63-82.

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Currently in the development of computer technologies that ensure information security and information protection, cryptographic methods of protection are widely used. The main tasks in cryptography are the development of new encryption features, difficult to break and repetitive ciphers. To solve that problem, falling into the class of NP-complete ones, algorithms based on natural principles have been used in recent years. These include genetic algorithms (GA), evolutionary methods, swarm intelligence algorithms. In models and algorithms of evolutionary computations, the construction of basic models and rules is implemented, according to which it can change (evolve). In recent years, evolutionary computing schemes have been proposed, including the genetic algorithm, genetic programming, evolutionary programming, and evolutionary strategies. The paper discusses the existing cryptography methods, basic concepts and methods of modern cryptography, the notion of a genetic algorithm, a universal hash function, as well as a hash detection method and a genetic hashing algorithm built on it. A genetic algorithm was implemented in the Golang language, modified for the current problem of finding the optimal hash functions. A detailed description of each stage of the algorithm execution is given. Also, within the framework of the research, a study of the function of the genetic algorithm itself and the genetic hashing algorithm was carried out, evaluating the convergence of the genetic algorithm depending on the input data, and evaluating the possible direction of further research.

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Patel, Roshni V., and Jignesh S. Patel. "Optimization of Linear Equations using Genetic Algorithms." Indian Journal of Applied Research 2, no. 3 (October 1, 2011): 56–58. http://dx.doi.org/10.15373/2249555x/dec2012/19.

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31

Lim, Siew Mooi, Abu Bakar Md Sultan, Md Nasir Sulaiman, Aida Mustapha, and K. Y. Leong. "Crossover and Mutation Operators of Genetic Algorithms." International Journal of Machine Learning and Computing 7, no. 1 (February 2017): 9–12. http://dx.doi.org/10.18178/ijmlc.2017.7.1.611.

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Shi, Jiahe. "Fourier Filtering Denoising Based on Genetic Algorithms." International Journal of Trend in Scientific Research and Development Volume-1, Issue-5 (August 31, 2017): 1142–62. http://dx.doi.org/10.31142/ijtsrd2420.

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CHIRIAC, Liubomir, Natalia LUPAŞCO, and Maria PAVEL. "Development of genetic algorithms from inter/transdisciplinary perspectives." Acta et commentationes: Științe ale Educației 33, no. 3 (September 2023): 31–42. http://dx.doi.org/10.36120/2587-3636.v33i3.31-42.

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The theoretical-practical foundations of Genetic Algorithms, which are built on the principle of "survival of the fittest", enunciated by Charles Darwin, are dealt with in this paper. The paper describes the basic characteristics of the genetic algorithm, highlighting its advantages and disadvantages. Genetic algorithm problems are examined. The Genetic Algorithm is examined from the perspective of examining problems in which finding the optimal solution is not simple or at least inefficient due to the characteristics of the probabilistic search. The steps are shown in which Genetic Algorithms encode a possible solution to a specific problem in a single data structure called a "chromosome" and set the stage for applying genetic operators to these structures in order to maintain critical information.

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Aivaliotis-Apostolopoulos, Panagiotis, and Dimitrios Loukidis. "Swarming genetic algorithm: A nested fully coupled hybrid of genetic algorithm and particle swarm optimization." PLOS ONE 17, no. 9 (September 23, 2022): e0275094. http://dx.doi.org/10.1371/journal.pone.0275094.

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Particle swarm optimization and genetic algorithms are two classes of popular heuristic algorithms that are frequently used for solving complex multi-dimensional mathematical optimization problems, each one with its one advantages and shortcomings. Particle swarm optimization is known to favor exploitation over exploration, and as a result it often converges rapidly to local optima other than the global optimum. The genetic algorithm has the ability to overcome local extrema throughout the optimization process, but it often suffers from slow convergence rates. This paper proposes a new hybrid algorithm that nests particle swarm optimization operations in the genetic algorithm, providing the general population with the exploitation prowess of the genetic algorithm and a sub-population with the high exploitation capabilities of particle swarm optimization. The effectiveness of the proposed algorithm is demonstrated through solutions of several continuous optimization problems, as well as discrete (traveling salesman) problems. It is found that the new hybrid algorithm provides a better balance between exploration and exploitation compared to both parent algorithms, as well as existing hybrid algorithms, achieving consistently accurate results with relatively small computational cost.

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Ran, Limin, Shengnan Ran, and Chunmei Meng. "Green city logistics path planning and design based on genetic algorithm." PeerJ Computer Science 9 (May 5, 2023): e1347. http://dx.doi.org/10.7717/peerj-cs.1347.

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Effective logistics distribution paths are crucial in enhancing the fundamental competitiveness of an enterprise. This research introduces the genetic algorithm for logistics routing to address pertinent research issues, such as suboptimal scheduling of time-sensitive orders and reverse distribution of goods. It proposes an enhanced scheme integrating the Metropolis criterion. To address the limited local search ability of the genetic algorithm, this study combines the simulated annealing algorithm’s powerful local optimization capability with the genetic algorithm, thereby developing a genetic algorithm with the Metropolis criterion. The proposed method preserves the optimal chromosome in each generation population and accepts inferior chromosomes with a certain probability, thereby enhancing the likelihood of finding an optimal local solution and achieving global optimization. A comparative study is conducted with the Ant Colony Optimization, Artificial Bee Colony, and Particle Swarm Optimization algorithms, and empirical findings demonstrate that the proposed genetic algorithm effectively achieves excellent results over these algorithms.

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Mitchell, Melanie, and Stephanie Forrest. "Genetic Algorithms and Artificial Life." Artificial Life 1, no. 3 (April 1994): 267–89. http://dx.doi.org/10.1162/artl.1994.1.3.267.

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Genetic algorithms are computational models of evolution that play a central role in many artificial-life models. We review the history and current scope of research on genetic algorithms in artificial life, giving illustrative examples in which the genetic algorithm is used to study how learning and evolution interact, and to model ecosystems, immune system, cognitive systems, and social systems. We also outline a number of open questions and future directions for genetic algorithms in artificial-life research.

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Liang, W. Y., and Peter O'Grady. "Genetic algorithms for design for assembly: The remote constrained genetic algorithm." Computers & Industrial Engineering 33, no. 3-4 (December 1997): 593–96. http://dx.doi.org/10.1016/s0360-8352(97)00200-3.

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LEE, In-Ho. "Modern Genetic Algorithms." Physics and High Technology 27, no. 1/2 (February 28, 2018): 8–11. http://dx.doi.org/10.3938/phit.27.002.

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Anderson-Cook, Christine M. "Practical Genetic Algorithms." Journal of the American Statistical Association 100, no. 471 (September 2005): 1099. http://dx.doi.org/10.1198/jasa.2005.s45.

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Stanoyevitch, Alexander. "Homogeneous genetic algorithms." International Journal of Computer Mathematics 87, no. 3 (March 2010): 476–90. http://dx.doi.org/10.1080/00207160801968770.

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Chiou, Yu-Chiun, and Lawrence W. Lan. "Genetic clustering algorithms." European Journal of Operational Research 135, no. 2 (December 2001): 413–27. http://dx.doi.org/10.1016/s0377-2217(00)00320-9.

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Harada, Tomohiro, and Enrique Alba. "Parallel Genetic Algorithms." ACM Computing Surveys 53, no. 4 (September 26, 2020): 1–39. http://dx.doi.org/10.1145/3400031.

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Baum, Eric B., Dan Boneh, and Charles Garrett. "Where Genetic Algorithms Excel." Evolutionary Computation 9, no. 1 (March 2001): 93–124. http://dx.doi.org/10.1162/10636560151075130.

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We analyze the performance of a genetic algorithm (GA) we call Culling, and a variety of other algorithms, on a problem we refer to as the Additive Search Problem (ASP). We show that the problem of learning the Ising perceptron is reducible to a noisy version of ASP. Noisy ASP is the first problem we are aware of where a genetic-type algorithm bests all known competitors. We generalize ASP to k-ASP to study whether GAs will achieve “implicit parallelism” in a problem with many more schemata. GAs fail to achieve this implicit parallelism, but we describe an algorithm we call Explicitly Parallel Search that succeeds. We also compute the optimal culling point for selective breeding, which turns out to be independent of the fitness function or the population distribution. We also analyze a mean field theoretic algorithm performing similarly to Culling on many problems. These results provide insight into when and how GAs can beat competing methods.

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Li, He, and Naiyu Shi. "Application of Genetic Optimization Algorithm in Financial Portfolio Problem." Computational Intelligence and Neuroscience 2022 (July 15, 2022): 1–9. http://dx.doi.org/10.1155/2022/5246309.

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In order to address the application of genetic optimization algorithms to financial investment portfolio issues, the optimal allocation rate must be high and the risk is low. This paper uses quadratic programming algorithms and genetic algorithms as well as quadratic programming algorithms, Matlab planning solutions for genetic algorithms, and genetic algorithm toolboxes to solve Markowitz’s mean variance model. The mathematical model for introducing sparse portfolio strategies uses the decomposition method of penalty functions as an algorithm for solving nonconvex sparse optimization strategies to solve financial portfolio problems. The merging speed of the quadratic programming algorithm is fast, and the merging speed depends on the selection of the initial value. The genetic algorithm performs very well in global searches, but local search capabilities are insufficient and the pace of integration into the next stage is slow. To solve this, using a genetic algorithm toolbox is quick and easy. The results of the experiments show that the final solution of the decomposition method of the fine function is consistent with the solution of the integrity of the genetic algorithm. 67% of the total funds will be spent on local car reserves and 33% on wine reserves. When data scales are small, quadratic programming algorithms and genetic algorithms can provide effective portfolio feedback, and the method of breaking down penalty functions to ensure the reliability and effectiveness of algorithm combinations is widely used in sparse financial portfolio issues.

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Hulianytskyi, Leonid, and Sergii Chornozhuk. "Genetic Algorithm with New Stochastic Greedy Crossover Operator for Protein Structure Folding Problem." Cybernetics and Computer Technologies, no. 2 (July 24, 2020): 19–29. http://dx.doi.org/10.34229/2707-451x.20.2.3.

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Introduction. The spatial protein structure folding is an important and actual problem in biology. Considering the mathematical model of the task, we can conclude that it comes down to the combinatorial optimization problem. Therefore, genetic and mimetic algorithms can be used to find a solution. The article proposes a genetic algorithm with a new greedy stochastic crossover operator, which differs from classical approaches with paying attention to qualities of possible ancestors. The purpose of the article is to describe a genetic algorithm with a new greedy stochastic crossover operator, reveal its advantages and disadvantages, compare the proposed algorithm with the best-known implementations of genetic and memetic algorithms for the spatial protein structure prediction, and make conclusions with future steps suggestion afterward. Result. The work of the proposed algorithm is compared with others on the basis of 10 known chains with a length of 48 first proposed in [13]. For each of the chain, a global minimum of free energy was already precalculated. The algorithm found 9 out of 10 spatial structures on which a global minimum of free energy is achieved and also demonstrated a better average value of solutions than the comparing algorithms. Conclusion. The quality of the genetic algorithm with the greedy stochastic crossover operator has been experimentally confirmed. Consequently, its further research is promising. For example, research on the selection of optimal algorithm parameters, improving the speed and quality of solutions found through alternative coding or parallelization. Also, it is worth testing the proposed algorithm on datasets with proteins of other lengths for further checks of the algorithm’s validity. Keywords: spatial protein structure, combinatorial optimization, genetic algorithms, crossover operator, stochasticity.

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Mishra, Bhabani Shankar Prasad, Subhashree Mishra, and Sudhansu Sekhar Singh. "Parallel Multi-Criterion Genetic Algorithms." International Journal of Applied Evolutionary Computation 7, no. 1 (January 2016): 50–62. http://dx.doi.org/10.4018/ijaec.2016010104.

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The objective of this paper is to study the existing and current research on parallel multi-objective genetic algorithms (PMOGAs) through an intensive experiment. Many early efforts on parallelizing multi-objective genetic algorithms were introduced to reduce the processing time needed to reach an acceptable solution of them with various examples. Further, the authors tried to identify some of the issues that have not yet been studied systematically under the umbrella of parallel multi-objective genetic algorithms. Finally, some of the potential application of parallel multi objective genetic algorithm is discussed.

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Ankita, Ankita, and Rakesh Kumar. "Hybrid Simulated Annealing: An Efficient Optimization Technique." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7s (July 13, 2023): 45–53. http://dx.doi.org/10.17762/ijritcc.v11i7s.6975.

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Genetic Algorithm falls under the category of evolutionary algorithm that follows the principles of natural selection and genetics, where the best adapted individuals in a population are more likely to survive and reproduce, passing on their advantageous traits to their offsprings. Crossover is a crucial operator in genetic algorithms as it allows the genetic material of two or more individuals in the population to combine and create new individuals. Optimizing it can potentially lead to better solutions and faster convergence of the genetic algorithm. The proposed crossover operator gradually changes the alpha value as the search proceeds, similar to the temperature in simulated annealing. The performance of the proposed crossover operator is compared with the simple arithmetic crossover operator. The experiments are conducted using Python and results show that the proposed crossover operator outperforms the simple arithmetic crossover operator. This paper also emphasizes the importance of optimizing genetic operators, particularly crossover operators, to improve the overall performance of genetic algorithms.

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Nurserik, D., F. R. Gusmanova, G. А. Abdulkarimova, and K. S. Dalbekova. "OVERVIEW OF HEURISTIC AND METAHEURISTIC ALGORITHMS." BULLETIN Series of Physics & Mathematical Sciences 71, no. 3 (September 30, 2020): 242–47. http://dx.doi.org/10.51889/2020-3.1728-7901.37.

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The article discusses the use of heuristic algorithms for optimization problems. The algorithms for stochastic optimization are described, which constitute the main properties of the metaheuristic and its classes. Evolutionary algorithms are described in general terms. In particular, the main steps and properties of genetic algorithms are presented. The main goal of this article is to solve the vehicle routing problem using a metaheuristic algorithm. The vehicle routing problem is a complex combinatorial NP-complete optimization problem. It is shown that the metaheuristic approach to solving the problem allows one to obtain a suboptimal solution without examining the entire space of possible solutions. The genetic algorithm belongs to the group of evolutionary algorithms. The definitions are briefly given to the terms characteristic of the genetic algorithm: gene, chromosome, personality (descendant), population, descendant operators, crossing, mutation, crossover. Application of the theory of finite automata in a genetic algorithm is described. The terminology and scheme of the genetic algorithm for solving various problems are proposed.

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Dharani Pragada, Venkata Aditya, Akanistha Banerjee, and Srinivasan Venkataraman. "OPTIMISATION OF NAVAL SHIP COMPARTMENT LAYOUT DESIGN USING GENETIC ALGORITHM." Proceedings of the Design Society 1 (July 27, 2021): 2339–48. http://dx.doi.org/10.1017/pds.2021.495.

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AbstractAn efficient general arrangement is a cornerstone of a good ship design. A big part of the whole general arrangement process is finding an optimized compartment layout. This task is especially tricky since the multiple needs are often conflicting, and it becomes a serious challenge for the ship designers. To aid the ship designers, improved and reliable statistical and computation methods have come to the fore. Genetic algorithms are one of the most widely used methods. Islier's algorithm for the multi-facility layout problem and an improved genetic algorithm for the ship layout design problem are discussed. A new, hybrid genetic algorithm incorporating local search technique to further the improved genetic algorithm's practicality is proposed. Further comparisons are drawn between these algorithms based on a test case layout. Finally, the developed hybrid algorithm is implemented on a section of an actual ship, and the findings are presented.

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Sherstnev, Pavel A., and Evgeniy S. Semenkin. "Self-configuring genetic programming algorithms with Success History-based Adaptation." Siberian Aerospace Journal 26, no. 1 (April 16, 2025): 60–70. https://doi.org/10.31772/2712-8970-2025-26-1-60-70.

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In this work, a novel method for self-tuning genetic programming (GP) algorithms is presented, based on the ideas of the Success History based Parameter Adaptation (SHA) method, originally developed for the Differential Evolution (DE) algorithm. The main idea of the method is to perform a dynamic analysis of the history of successful solutions to adapt the algorithm's parameters during the search process. To implement this concept, the operation scheme of classical GP was modified to mimic the DE scheme, allowing the integration of the success history mechanism into GP. The resulting algorithm, denoted as SHAGP (Success-History based Adaptive Genetic Programming), demonstrates new capabilities for parameter adaptation, such as the adjustment of crossover and mutation probabilities. The work also includes a detailed review of existing self-tuning methods for GP algorithms, which allowed for the identification of their key advantages and limitations and the application of this knowledge in the development of SHAGP. Additionally, new crossover operators are proposed that enable dynamic adjustment of the crossover probability, account for the selective pressure at the current stage, and implement a multi-parent approach. This modification allows for more flexible control over the process of genotype recombination, thereby enhancing the algorithm's adaptability to the problem at hand. To adjust the probabilities of applying various operators (selection, crossover, mutation), self-configuring evolutionary algorithm methods are employed, in particular, the Self-Configuring Evolutionary Algorithm and the Population-Level Dynamic Probabilities Evolutionary Algorithm. Within the framework of this work, two variants of the algorithm were implemented – SelfCSHAGP and PDPSHAGP. The efficiency of the proposed algorithms was tested on problem sets from the Feynman Symbolic Regression Database. Each algorithm was run multiple times on each problem to obtain a reliable statistical sample, and the results were compared using the Mann–Whitney statistical test. The experimental data showed that the proposed algorithms achieve a higher reliability metric compared to existing GP self-tuning methods, with the PDPSHAGP method demonstrating the best efficiency in more than 90 % of the cases. Such a universal self-tuning mechanism can find applications in a wide range of fields, such as automated machine learning, big data processing, engineering design, and medicine, as well as in space applications – for example, in the design of navigation systems for spacecraft and the development of control systems for aerial vehicles. In these areas, the high reliability of algorithms and their ability to find optimal solutions in complex multidimensional spaces are critically important.

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