A Synergistic Approach for Evolutionary Optimization

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Abstract

One of the major causes of premature convergence in Evolutionary Algorithm (EA) is loss of population diversity, which pushes the search space to a homogeneous or a near-homogeneous configuration. In particular, this can be a more complicated issue in case of high dimensional complex problem domains. In [13, 14], we presented two novel EA frameworks to curb premature convergence by maintaining constructive diversity in the population. The COMMUNITY_GA or COUNTER_NICHING_GA in [13] uses an informed exploration technique to maintain constructive diversity. In addition to this, the POPULATION_GA model in [14] balances exploration and exploitation using a hierarchical multi-population approach. The current research presents further investigation on the later model which synergistically uses an exploration controlling mechanism through informed genetic operators along with a multi-tier hierarchical dynamic population architecture, which allows initially less fit individuals a fair chance to survive and evolve. Simulations using a set of popular benchmark test functions showed promising results.
Original languageEnglish
Title of host publicationGECCO 2008
Place of PublicationUSA
PublisherACM Press
Pages2105-2110
Number of pages6
ISBN (Electronic)9781605581316
Publication statusPublished - 2008
EventGenetic and Evolutionary Computation Conference - USA, New Zealand
Duration: 12 Jul 200816 Jul 2008

Conference

ConferenceGenetic and Evolutionary Computation Conference
CountryNew Zealand
Period12/07/0816/07/08

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  • Cite this

    Bhattacharya, M. (2008). A Synergistic Approach for Evolutionary Optimization. In GECCO 2008 (pp. 2105-2110). ACM Press.