Diversity Preservation in Genetic Algorithm by Lifespan Control

  • Yu YamaneEmail author
  • Masataka Seo
  • Ikuko Nishikawa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)


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.


Genetic algorithm Diversity Combinatorial optimization Lifespan 


  1. 1.
    Goldberg, E.D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Boston (1989)zbMATHGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Kitano, H.: Continuous generation genetic algorithms. J. Soc. Instrum. Control Eng. 32(1), 31–38 (1993)Google Scholar
  5. 5.
    De Jong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. Doctoral Dissertation, University of Michigan (1975)Google Scholar
  6. 6.
    Syswerda, G.: Unifrom crossover in genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 2–9 (1989)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Ritsumeikan UniversityKusatsuJapan

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