Adaptive Control of the Number of Crossed Genes in Many-Objective Evolutionary Optimization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Hiroyuki Sato , Carlos A. Coello Coello , Hernán E. Aguirre , Kiyoshi Tanaka

ABSTRACT

To realize effective genetic operation in evolutionary many-objective optimization, crossover controlling the number of crossed genes (CCG) has been proposed. CCG controls the number of crossed genes by using an user-defined parameter α. CCG with small α significantly improves the search performance of multi-objective evolutionary algorithm in many-objective optimization by keeping small the number of crossed genes. However, to achieve high search performance by using CCG, we have to find out an appropriate parameter α by conducting many experiments. To avoid parameter tuning and automatically find out an appropriate α in a single run of the algorithm, in this work we propose an adaptive CCG which adopts the parameter α during the solutions search. Simulation results show that the values of α controlled by the proposed method converges to an appropriate value even when the adaptation is started from any initial values. Also we show the adaptive CCG achieves more than 80% with a single run of the algorithm for the maximum search performance of the static CCG using an optimal α *. More... »

PAGES

478-484

References to SciGraph publications

  • 1998. Multiobjective optimization using evolutionary algorithms — A comparative case study in PARALLEL PROBLEM SOLVING FROM NATURE — PPSN V
  • Book

    TITLE

    Learning and Intelligent Optimization

    ISBN

    978-3-642-34412-1
    978-3-642-34413-8

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-34413-8_48

    DOI

    http://dx.doi.org/10.1007/978-3-642-34413-8_48

    DIMENSIONS

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