research-article

A synergistic approach for evolutionary optimization

Author:

Maumita BhattacharyaAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computation

Pages 2105 - 2110

https://doi.org/10.1145/1388969.1389031

Published: 12 July 2008 Publication History

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.

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Cited By

Pellegrini PFavaretto D(2012)Quantifying the exploration performed by metaheuristicsJournal of Experimental & Theoretical Artificial Intelligence10.1080/0952813X.2012.65632724:2(247-266)Online publication date: Jun-2012
https://doi.org/10.1080/0952813X.2012.656327
Favaretto DMoretti EPellegrini P(2009)On the Explorative Behavior of MAX---MIN Ant SystemProceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics10.1007/978-3-642-03751-1_10(115-119)Online publication date: 3-Sep-2009
https://dl.acm.org/doi/10.1007/978-3-642-03751-1_10

Index Terms

A synergistic approach for evolutionary optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computation

July 2008

1182 pages

ISBN:9781605581316

DOI:10.1145/1388969

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

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Published: 12 July 2008

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July 12 - 16, 2008

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Cited By

Pellegrini PFavaretto D(2012)Quantifying the exploration performed by metaheuristicsJournal of Experimental & Theoretical Artificial Intelligence10.1080/0952813X.2012.65632724:2(247-266)Online publication date: Jun-2012
https://doi.org/10.1080/0952813X.2012.656327
Favaretto DMoretti EPellegrini P(2009)On the Explorative Behavior of MAX---MIN Ant SystemProceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics10.1007/978-3-642-03751-1_10(115-119)Online publication date: 3-Sep-2009
https://dl.acm.org/doi/10.1007/978-3-642-03751-1_10

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