Genetic algorithm design inspired by organizational theory: Pilot study of a dependency structure matrix driven genetic algorithm

Tian Li Yu, David E. Goldberg, Ali Yassine, Ying Ping Chen

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    17 Scopus citations

    Abstract

    This study proposes a dependency structure matrix driven genetic algorithm (DSMDGA) which utilizes the dependency structure matrix (DSM) clustering to extract building block (BB) information and use the information to accomplish BB-wise crossover. Three cases: tight, loose, and random linkage, are tested on both a DSMDGA and a simple genetic algorithm (SGA). Experiments showed that the DSMDGA is able to correctly identify BBs and outperforms a SGA.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    EditorsErick Cantú-Paz, James A. Foster, Graham Kendall, Mark Harman, Dipankar Dasgupta, Kalyanmoy Deb, Lawrence David Davis, Rajkumar Roy, Una-May O'Reilly, Hans-Georg Beyer, Russell Standish, Stewart Wilson, Joachim Wegener, Mitch A. Potter, Alan C. Schultz, Kathryn A. Dowsland, Natasha Jonoska, Julian Miller
    Pages1620-1621
    Number of pages2
    DOIs
    StatePublished - 2003

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume2724
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

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