Abstract
In a previous work, we introduced a population-based, bio-inspired algorithm. The proposed algorithm is inspired by the biological animal life cycle, consisting of the birth, growth, reproduction, and death stages. Our algorithm was initially based on the canonical Genetic Algorithm (GA), where all the individuals have a genotype (chromosome). One difference to highlight in our algorithm is that both the crossing and the mutation are executed through independent processes that randomly affect the population. This paper focuses on breeding, whereas in earlier versions of the algorithm, we used the traditional GA one-point crossover. In this paper, we propose a different alternative to the classical approach, where part of the genetic information is directly copied to each of the offspring in the crossover operator, where this type of crossover may not perform well in continuous optimization problems. In this proposal, we use the parent’s genetic information for each gene, using those values as lower and upper bounds of a range, where a random value within that range determines the new value for that gene index of the offspring. This is similar to what is used by algorithms such as Differential Evolution, where we consider our proposal as a variation of existing proposals. We expect this new operator to allow the offspring to continue exploring new search spaces with the birth of individuals. In this paper, we use the benchmark functions introduced in the Competition on Evolutionary Computation for the 2017 edition (CEC-2017) to compare the traditional one-point crossover and our proposed strategy. Experimental results indicate that our proposed operator may be a good alternative for the canonical crossover.
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References
Castillo, O., Valdez, F., Soria, J., Amador-Angulo, L., Ochoa, P., Peraza, C.: Comparative study in fuzzy controller optimization using bee colony, differential evolution, and harmony search algorithms. Algorithms 12(1), 9 (2019)
Valdez, F.: Swarm intelligence: a review of optimization algorithms based on animal behavior. In: Recent Advances of Hybrid Intelligent Systems Based on Soft Computing, pp. 273–298 (2021)
Acherjee, B., Maity, D., Kuar, A.S.: Ultrasonic machining process optimization by cuckoo search and chicken swarm optimization algorithms. Int. J. Appl. Metaheuristic Comput. (IJAMC) 11(2), 1–26 (2020)
Porto, V.W.: Evolutionary programming. In: Evolutionary Computation 1, pp. 127–140. CRC Press (2018)
Back, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming. Oxford University Press, Genetic Algorithms (1996)
Valdez, M.G., Guervós, J.J.M.: A container-based cloud-native architecture for the reproducible execution of multi-population optimization algorithms. Futur. Gener. Comput. Syst. 116, 234–252 (2021)
García-Valdez, M., Trujillo, L., Merelo, J.J., de Vega, F.F., Olague, G.: The evospace model for pool-based evolutionary algorithms. J. Grid Comput. 13(3), 329–349 (2015)
Merelo, J.J., García-Valdez, M., Castillo, P.A., García-Sánchez, P., Cuevas, P., Rico, N.: Nodio, a javascript framework for volunteer-based evolutionary algorithms: first results. ar**v:1601.01607 (2016)
Felix-Saul, J.C., Valdez, M.G., Guervós, J.J.M.: A Novel Distributed Nature-Inspired Algorithm for Solving Optimization Problems, pp. 107–119. Springer International Publishing, Cham (2022)
Felix-Saul, J.C., Garcia Valdez, M.: Recovering from Population Extinction in the Animal Life Cycle Algorithm (ALCA), pp. 425–440. Springer Nature Switzerland, Cham (2023)
Read, K., Ashford, J.: A system of models for the life cycle of a biological organism. Biometrika 55(1), 211–221 (1968)
Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–73 (1992)
Holland, J.H.: Genetic algorithms and adaptation. In: Adaptive Control of Ill-defined Systems, pp. 317–333 (1984)
Awad, N., Ali, M., Liang, J., Qu, B., Suganthan, P.: Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective bound constrained real-parameter numerical optimization. In: Technical Report, pp. 1–34. Nanyang Technological University Singapore (2016)
Venter, G., Sobieszczanski-Sobieski, J.: Particle swarm optimization. AIAA J. 41(8), 1583–1589 (2003)
Wang, D., Tan, D., Liu, L.: Particle swarm optimization algorithm: an overview. Soft Comput. 22, 387–408 (2018)
McDevitt, L.J., Ombuki-Berman, B.M., Engelbrecht, A.P.: A particle swarm optimization decomposition strategy for large scale global optimization. In: 2022 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1574–1581. IEEE (2022)
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TecNM Project 18186.23-P has partially funded this research.
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Felix-Saul, J.C., García-Valdez, M. (2024). A New Breeding Crossover Approach for Evolutionary Algorithms. In: Castillo, O., Melin, P. (eds) New Horizons for Fuzzy Logic, Neural Networks and Metaheuristics. Studies in Computational Intelligence, vol 1149. Springer, Cham. https://doi.org/10.1007/978-3-031-55684-5_15
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