Article

Enhancing the Efficiency of the ECGA

Authors:

Thyago S. Duque,

David E. Goldberg,

Kumara SastryAuthors Info & Claims

Proceedings of the 10th International Conference on Parallel Problem Solving from Nature --- PPSN X - Volume 5199

Pages 165 - 174

Published: 13 September 2008 Publication History

Abstract

In this paper we show preliminary results of two efficiency enhancements proposed for Extended Compact Genetic Algorithm. First, a model building enhancement was used to reduce the complexity of the process from On ³ to On ², speeding up the algorithm by 1000 times on a 4096 bits problem. Then, a local-search hybridization was used to reduce the population size by at least 32 times, reducing the memory and running time required by the algorithm. These results are the first steps toward a competent and efficient Genetic Algorithm.

References

[1]

Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading 1989

Digital Library

[2]

Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge 1975

Digital Library

[3]

Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Dordrecht 2002

Digital Library

[4]

Sastry, K., Goldberg, D., Pelikan, M.: Efficiency enhancment of probabilistic model building genetic algorithm. Technical report, Illinois Genetic Algorithms Laboratory, Univeristy of Illinois at Urbana Champaign, Urbana, IL 2004

[5]

Cantu-Paz, E.: Designing Efficient and Accurate Parallel Genetic Algorithms. PhD thesis, University of Illinois at Urbana-Champaign, Illigal Report No 99017 1999

Digital Library

[6]

Goldberg, D.E., Voessner, S.: Optimizing Global-Local Search Hybrids. In: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 1, pp. 220---228. Morgan Kaufmann, San Francisco 1999

Digital Library

[7]

Sinha, A., Goldberg, D.: A survey of hybrid genetic and evolutionary algorithms. Technical report, University of Illinois at Urbana Chapaign, Urbana, IL 1999 IlliGal Report No. 2003004

[8]

Sinha, A.: Designing efficient genetic and evolutionary algorithm hybrids, Master Thesis, University of Illinois at Urbana Champaign 2003 IlliGal Report No. 2003020

[9]

Sastry, K., Goldberg, D.: Let's Get Ready to Rumble: Crossover Versus Mutation Head to Head. In: Deb, K., et al. eds. GECCO 2004. LNCS, vol. 3103. Springer, Heidelberg 2004

[10]

Sastry, K.: Evaluation-relaxation Schemes for Genetic and Evolutionary Algorithms. PhD thesis, University of Illinois at Urbana-Champaign 2001

[11]

Harik, G., Lobo, F., Goldberg, D.: The compact genetic algorithm. In: Proceedings of IEEE Iternational Conference on Evolutionary Computation 1998, pp. 523---528 1998

[12]

Goldberg, D., Sastry, K., Llorí, X.: Toward routine billion-variable optimization using genetic algorithms: Short Communication. Complexity 123, 27---29 2007

Digital Library

[13]

Sastry, K., Goldberg, D., Llora, X.: Towards billion-bit optimization via a parallel estimation of distribution algorithm. In: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 577---584 2007

Digital Library

[14]

Harik, G.: Linkage Learning via probabilistic modeling in the ECGA. Technical report, University of Illinois at Urbana Chapaign, Urbana, IL 1999

[15]

Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Dordrecht 2001

Digital Library

[16]

Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: BOA: The Bayesian optimization algorithm. In: Proceedings of the Genetic And Evolutionary Computation Conference, pp. 524---532 1999

Digital Library

[17]

de la Ossa, L., Sastry, K., Lobo, F.: ---ary Extended Compact Genetic Algorithm in C++. Technical report, Illigal Report 2006013, Illinois Genetic Algorithms Lab, University of Illinois at Urbana-Champaign 2006

[18]

Thierens, D., Goldberg, D.: Mixing in Genetic Algorithms. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 38---47 1993

Digital Library

[19]

Pelikan, M., Sastry, K., Goldberg, D.: Sporadic model building for efficiency enhancement of hierarchical BOA. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 405---412. ACM Press, New York 2006

Digital Library

[20]

Lima, C., Sastry, K., Goldberg, D., Lobo, F.: Combining competent crossover and mutation operators: a probabilistic model building approach. In: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 735---742 2005

Digital Library

[21]

Yu, T., Goldberg, D.: Conquering hierarchical difficulty by explicit chunking: substructural chromosome compression. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 1385---1392 2006

Digital Library

Cited By

Iclănzan DDumitrescu D(2010)Graph clustering based model buildingProceedings of the 11th international conference on Parallel problem solving from nature: Part I10.5555/1885031.1885086(506-515)Online publication date: 11-Sep-2010
https://dl.acm.org/doi/10.5555/1885031.1885086
Santana RLarraòaga PLozano J(2010)Learning factorizations in estimation of distribution algorithms using affinity propagationEvolutionary Computation10.1162/EVCO_a_0000218:4(515-546)Online publication date: 1-Dec-2010
https://dl.acm.org/doi/10.1162/EVCO_a_00002

Recommendations

Easy Efficiency-Enhancement Technique for the ECGA
SBRN '08: Proceedings of the 2008 10th Brazilian Symposium on Neural Networks

Several works have shown that population size affects the performance and efficiency of evolutionary optimization algorithms. There are works in the literature proposing techniques for the determination of the best size of a population using complex ...
Sub-structural niching in estimation of distribution algorithms
GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

We propose a sub-structural niching method that fully exploits the problem decomposition capability of linkage-learning methods such as the estimation distribution algorithms and concentrate on maintaining diversity at the sub-structural level. The ...
Improving the efficiency of the extended compact genetic algorithm
GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Evolutionary Algorithms are largely used search and optimization procedures that, when properly designed, can solve intractable problems in tractable polynomial time. Efficiency enhancements are used to turn them from tractable to practical. In this ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

Proceedings of the 10th International Conference on Parallel Problem Solving from Nature --- PPSN X - Volume 5199

September 2008

1159 pages

ISBN:9783540876991

Editors:
Günter Rudolph
Fakultät für Informatik, Technische Universität Dortmund, Dortmund, Germany
,
Thomas Jansen
Fakultät für Informatik, Technische Universität Dortmund, Dortmund, Germany
,
Nicola Beume
Fakultät für Informatik, Technische Universität Dortmund, Dortmund, Germany
,
Simon Lucas
Department of Computing and Electronic Systems, University of Essex, Colchester, UK
,
Carlo Poloni
Dipartimento di Ingegneria Meccanica, Universití degli Studi di Trieste, Trieste, Italy

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 13 September 2008

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Iclănzan DDumitrescu D(2010)Graph clustering based model buildingProceedings of the 11th international conference on Parallel problem solving from nature: Part I10.5555/1885031.1885086(506-515)Online publication date: 11-Sep-2010
https://dl.acm.org/doi/10.5555/1885031.1885086
Santana RLarraòaga PLozano J(2010)Learning factorizations in estimation of distribution algorithms using affinity propagationEvolutionary Computation10.1162/EVCO_a_0000218:4(515-546)Online publication date: 1-Dec-2010
https://dl.acm.org/doi/10.1162/EVCO_a_00002

View Options

View options

Media

Figures

Other

Tables

View Table of Contents