A Clustering-Based Model-Building EA for Optimization Problems with Binary and Real-Valued Variables

Krzysztof L. Sadowski, Peter A. N. Bosman, Dirk Thierens

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Abstract

    We propose a novel clustering-based model-building evolutionary
    algorithm to tackle optimization problems that
    have both binary and real-valued variables. The search
    space is clustered every generation using a distance metric
    that considers binary and real-valued variables jointly
    in order to capture and exploit dependencies between variables
    of different types. After clustering, linkage learning
    takes place within each cluster to capture and exploit dependencies
    between variables of the same type. We compare
    this with a model-building approach that only considers dependencies
    between variables of the same type. Additionally,
    since many real-world problems have constraints, we
    examine the use of different well-known approaches to handling
    constraints: constraint domination, dynamic penalty
    and global competitive ranking. We experimentally analyze
    the performance of the proposed algorithms on various unconstrained
    problems as well as a selection of well-known
    MINLP benchmark problems that all have constraints, and
    compare our results with the Mixed-Integer Evolution Strategy
    (MIES). We find that our approach to clustering that is
    aimed at the processing of dependencies between binary and
    real-valued variables can significantly improve performance
    in terms of required population size and function evaluations
    when solving problems that exhibit properties such as multiple
    optima, strong mixed dependencies and constraints.
    Original languageEnglish
    Title of host publicationProceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, July 11-15, 2015
    EditorsJuan Luis Jiménez Laredo, Sara Silva, Anna Isabel Esparcia-Alcázar
    PublisherAssociation for Computing Machinery
    Pages911-918
    Number of pages8
    ISBN (Print)978-1-4503-3472-3
    DOIs
    Publication statusPublished - 2015

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