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Testing parallelization paradigms for MOEAs

Authors:

Sadeesha Gamhewa,

Philip F. HingstonAuthors Info & Claims

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

Pages 755 - 756

https://doi.org/10.1145/1389095.1389240

Published: 12 July 2008 Publication History

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Abstract

In this paper, we report on our investigation of factors affecting the performance of various parallelization paradigms for multi-objective evolutionary algorithms. Different parallelization paradigms emphasize separate development of sub-populations versus communication and coordination between sub-populations to greater or lesser degrees. We hypothesized that the characteristics of a particular problem will favour some paradigms over others. We tested this hypothesis by creating variations on test problems with different characteristics, and testing the performance of different paradigms in a cluster environment.

References

[1]

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II {Electronic version}. IEEE Transactions on Evolutionary Computation, 6(2), 182--197.

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[2]

Huband, S., Barone, L., While, L., & Hingston, P. (2005). A Scalable Multi-objective Test Problem Toolkit. In Proceedings of Evolutionary Multi-Criterion Optimisation, Third International Conference (pp. 280--294). Berlin: Springer--Verlag.

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[3]

Huband, S., Hingston, P., Barone, L., & While, L. (2006). A Review of Multiobjective Test Problems and a Scalable Test Problem Toolkit {Electronic version}. IEEE Transactions on Evolutionary Computation, 10(5), 477--506.

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[4]

Van Veldhuizen, D. A., Zydallis, J. B., & Lamont, G. B. (2003). Considerations in Engineering Parallel Multiobjective Evolutionary Algorithms {Electronic version}. IEEE Transactions on Evolutionary Computation, 7(2), 144--173.

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Index Terms

Testing parallelization paradigms for MOEAs
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

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

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Recommendations

A Study for Parallelization of Multi-Objective Evolutionary Algorithm Based on Decomposition and Directed Mating

Asynchronous master-slave parallelization of differential evolution for multi-objective optimization

The Kremlin Oracle for Sequential Code Parallelization

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