Abstract
Genetic algorithms (GA) application to a real world problem which possesses various complicated constraints often requires to design each problem dependent genetic operations to keep the feasibility of the individual, which could decrease the population diversity and cause the early convergence. We propose Life Control Genetic Algorithm (LCGA) to maintain the diversity under such biased operations, by setting the lifespan of each individual depending on the relative fitness. LCGA is first applied to a typical functional optimization with largely biased crossover, and compared with several types of GA including uniform lifespan. Next, it is applied to a building design optimization as an example of a real world combinatorial optimization with complicated constraints, and the effectiveness is studied by numerical experiments.
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References
Goldberg, E.D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Boston (1989)
Kubota, N., Date, T., Fukuda, T.: Introduction of age structure to genetic algorithm and its convergence. Trans. Soc. Instrum. Control Eng. 31(5), 560–568 (1995)
Yoshitomi, S., Nakagawa, D., Sada, T.: Research on structural optimization for steel industrialized housing - practical method for optimal placement of structural members based on genetic algorithm. Archit. Inst. Japan J. Struct. Constr. Eng. 80(714), 1347–1355 (2015)
Kitano, H.: Continuous generation genetic algorithms. J. Soc. Instrum. Control Eng. 32(1), 31–38 (1993)
De Jong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. Doctoral Dissertation, University of Michigan (1975)
Syswerda, G.: Unifrom crossover in genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 2–9 (1989)
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Yamane, Y., Seo, M., Nishikawa, I. (2020). Diversity Preservation in Genetic Algorithm by Lifespan Control. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_69
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DOI: https://doi.org/10.1007/978-3-030-32456-8_69
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