Migrational GA that preserves solutions in non-static optimization problems

Pitoyo Hartono, Shuji Hashimoto

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    GA has been successfully introduced to solve various optimizations problems. One of the characteristics of GA is that once it has converged, most of its population members will be copies of the best individual, causing GA to loose population diversity. This characteristic is a setback when we consider non-stationary problems in which the fitness functions vary with time. In this paper we propose Migrational-GA that stores the past environmental solutions and retrieved them rapidly when that environment is reactivated, through probabilistic operation.

    Original languageEnglish
    Title of host publicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
    Pages255-260
    Number of pages6
    Volume1
    Publication statusPublished - 2001
    Event2001 IEEE International Conference on Systems, Man and Cybernetics - Tucson, AZ, United States
    Duration: 2001 Oct 72001 Oct 10

    Other

    Other2001 IEEE International Conference on Systems, Man and Cybernetics
    CountryUnited States
    CityTucson, AZ
    Period01/10/701/10/10

    Keywords

    • Environmental change
    • Main and subpopulations
    • Migrational GA
    • Non-stationary optimization problems

    ASJC Scopus subject areas

    • Hardware and Architecture
    • Control and Systems Engineering

    Cite this

    Hartono, P., & Hashimoto, S. (2001). Migrational GA that preserves solutions in non-static optimization problems. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (Vol. 1, pp. 255-260)