Abstract
The choice in selection, crossover and mutation operators can significantly impact the performance of a genetic algorithm (GA). It is found that the optimal combination of these operators are dependent on the problem characteristics and the size of the problem space. However, existing works disregard the above and focus only on introducing adaptiveness in one operator while having other operators static. With adaptive operator selection (AOS), this paper presents a novel framework for an adaptive and modular genetic algorithm (AMGA) to discover the optimal combination of the operators in each stage of the GA’s life in order to avoid premature convergence. In AMGA, the selection operator changes in an online manner to adapt the selective pressure, while the best performing crossover and mutation operators are inherited by the offspring of each generation. Experimental results demonstrate that our AMGA framework is able to find the optimal combinations of the GA operators for each generation for different instances of the traveling salesman problem (TSP) and outperforms the existing AOS models.
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References
Oliver, I., Smith, D., Holland, J.R.: Study of permutation crossover operators on the traveling salesman problem. In: ICGA (1987)
Murata, T., Ishibuchi, H.: Performance evaluation of genetic algorithms for flowshop scheduling problems. In: CEC, pp. 812–817 (1994)
Abdoun, O., Abouchabaka, J., Tajani, C.: Analyzing the performance of mutation operators to solve the travelling salesman problem. Int. J. Emerg. Sci. 2, 61–77 (2012)
Abdoun, O., Abouchabaka, J.: A comparative study of adaptive crossover operators for genetic algorithms to resolve the traveling salesman problem. Int. J. Comput. Appl. 31(11), 49–57 (2011)
Razali, N.M., Geraghty, J., et al.: Genetic algorithm performance with different selection strategies in solving TSP. In: WCE, vol. 2, pp. 1134–1139 (2011)
Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In: Foundations of Genetic Algorithms, vol. 1, pp. 69–93 (1991)
Črepinšek, M., Liu, S.H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 35 (2013)
Karafotias, G., Hoogendoorn, M., Eiben, Á.E.: Parameter control in evolutionary algorithms: trends and challenges. CEC 19(2), 167–187 (2015)
Spears, W.M.: Adapting crossover in evolutionary algorithms. In: Evolutionary Programming, pp. 367–384 (1995)
Riff, M.C., Bonnaire, X.: Inheriting parents operators: a new dynamic strategy for improving evolutionary algorithms. In: International Symposium on Methodologies for Intelligent Systems, pp. 333–341. Springer (2002)
Gomez, J.: Self adaptation of operator rates in evolutionary algorithms. In: Genetic and Evolutionary Computation Conference, pp. 1162–1173. Springer (2004)
Cruz-Salinas, A.F., Perdomo, J.G.: Self-adaptation of genetic operators through genetic programming techniques. In: GECCO, pp. 913–920. ACM (2017)
Montero, E., Riff, M.C.: Self-calibrating strategies for evolutionary approaches that solve constrained combinatorial problems. In: International Symposium on Methodologies for Intelligent Systems, pp. 262–267. Springer (2008)
Montero, E., Riff, M.C.: On-the-fly calibrating strategies for evolutionary algorithms. Inf. Sci. 181(3), 552–566 (2011)
Osaba, E., Diaz, F., Onieva, E., Carballedo, R., Perallos, A.: AMCPA: a population metaheuristic with adaptive crossover probability and multi-crossover mechanism for solving combinatorial optimization problems. Int. J. Artif. Intell. 12(2), 1–23 (2014)
Osaba, E., Onieva, E., Carballedo, R., Diaz, F., Perallos, A.: An adaptive multi-crossover population algorithm for solving routing problems. In: Nature Inspired Cooperative Strategies for Optimization, pp. 113–124 (2014)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)
Whitley, L.D., et al.: The genitor algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In: Proceedings of the 3rd International Conference on Genetic Algorithms, vol. 89, pp. 116–123 (1989)
Baker, J.E.: Reducing bias and inefficiency in the selection algorithm. In: Proceedings of the 2nd International Conference on Genetic Algorithms and their Application, pp. 14–21 (1987)
Davis, L.: Applying adaptive algorithms to epistatic domains. In: International Joint Conference on Artificial Intelligence, vol. 85, pp. 162–164 (1985)
Whitley, D., Starkweather, T., Shaner, D.: The traveling salesman and sequence scheduling: quality solutions using genetic edge recombination (1991)
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Ohira, R., Islam, M.S., Jo, J., Stantic, B. (2020). AMGA: An Adaptive and Modular Genetic Algorithm for the Traveling Salesman Problem. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_107
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DOI: https://doi.org/10.1007/978-3-030-16660-1_107
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