A framework for studying the effects of dynamic crossover, mutation, and population sizing in genetic algorithms

Michael A. Lee, Hideyuki Takagi

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

    6 Citations (Scopus)

    Abstract

    We introduce a framework for controlling genetic algorithms and use it to study the effect of dynamically modulating genetic algorithm control . parameters on search behavior. Our framework includes techniques that can automatically design control strategies for genetic algorithms according to a given search performance metric. Many of the automatically designed strategies exhibit an exponentially decreasing mutation rate behavior. We present experimental results indicating that exponentially decreasing the mutation rate over time contributes more towards an increase on online and offiine search performance than populations size or crossover rate modulation.

    Original languageEnglish
    Title of host publicationAdvances in Fuzzy Logic, Neural Networks and Genetic Algorithms - IEEE/Nagoya-University World Wisepersons Workshop, 1994, Selected Papers
    EditorsTakeshi Furuhashi
    PublisherSpringer Verlag
    Pages111-126
    Number of pages16
    ISBN (Print)9783540606079
    DOIs
    Publication statusPublished - 1995
    Event3rd World Wisepersons Workshop on Fuzzy Logic and Neural Networks/Genetic Algorithms, WWW 1994 - Nagoya, Japan
    Duration: Aug 9 1994Aug 10 1994

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume1011
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other3rd World Wisepersons Workshop on Fuzzy Logic and Neural Networks/Genetic Algorithms, WWW 1994
    Country/TerritoryJapan
    CityNagoya
    Period8/9/948/10/94

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • General Computer Science

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