Article

An extended mutation concept for the local selection based differential evolution algorithm

Authors:

Jani Ilmari Rönkkönen,

Jouni LampinenAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

Pages 689 - 696

https://doi.org/10.1145/1276958.1277101

Published: 07 July 2007 Publication History

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Abstract

A new mutation concept is proposed to generalize local selection based Differential Evolution algorithm to work in general multi-modal problems. Three variations of the proposed method are compared with classic Differential Evolution algorithm using a set of five well known test functions and their variants. The general idea of the new mutation operation is to divide the mutation into two parts: the local and global mutation. The global mutation works as a migration operator allowing the algorithm perform global search efficiently, while the local mutation improves the efficiency of local search.

The results show that the concept of global mutation is able to generalize the good performance of local selection based Differential Evolution from convex uni-modal functions to general non-convex and multi-modal problems. Among the tested functions, the new method was able to outperform the classic Differential Evolution in all butone. A limited analysis of the effects of control parameters to the performance of the algorithm is also done.

References

[1]

K. Deb, A. Anand, and D. Joshi. A computationally efficient evolutionary algorithm for real-parameter optimization. Evolutionary Computation, 10(4):371--395, 2002.

Digital Library

Google Scholar

[2]

N. Hansen and A. Ostermeier. Completely derandomized self adaptation in evolution strategies. Evolutionary Computation, 9(2):159--195, 2001.

Digital Library

Google Scholar

[3]

S. Kukkonen and J. Lampinen. Constrained real-parameter optimization with generalized differential evolution. In Proceedings of the 2006 Congress on Evolutionary Computation (CEC 2006), pages 911--918, Vancouver, BC, Canada, July 2006.

Crossref

Google Scholar

[4]

J. Lampinen and R. Storn. New Optimization Techniques in Engineering, chapter Differential Evolution, pages 123--166. Springer-Verlag, 2004. ISBN: 3-540-20167.

Google Scholar

[5]

J. Lampinen and I. Zelinka. On stagnation of the differential evolution algorithm. In Proceedings of MENDEL 2000, 6th International Conference on Soft Computing, pages 76--83, Brno, Czech Republic, 7--9 June. ISBN 80-214-1609-2.

Google Scholar

[6]

K. Price. An introduction to differential evolution. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 79--108. McGrawHill. ISBN 007-709506-5.

Digital Library

Google Scholar

[7]

K. V. Price and J. I. Rönkkönen. Comparing the uni-modal scaling performance of global and local selection in mutation-only differential evolution algorithm. In Proceedings of 2006 IEEE World Congress on Computational Intelligence, pages 7387--7394, Vancouver, Canada, 16--21 July 2006. ISBN 0-7803-9489-5.

Crossref

Google Scholar

[8]

K. V. Price, R. M. Storn, and J. A. Lampinen. Differential Evolution: A Practical Approach to Global Optimization. Springer, 2005. ISBN 3--540--20950--6.

Digital Library

Google Scholar

[9]

G. Rudolph. Convergence of evolutionary algorithms in general search spaces. In Proceedings of the third IEEE Conference of Evolutionary Computation, pages 50--54. IEEE Press.

Google Scholar

[10]

J. Rönkkönen, S. Kukkonen, and J. Lampinen. A comparison of differential evolution and generalized generation gap algorithms. Journal of Advanced Computational Intelligence and Intelligent Informatics, 9(5), September 2005. ISSN 1343--0130.

Crossref

Google Scholar

[11]

J. Rönkkönen and J. Lampinen. On using normally distributed mutation step lenght for the differential evolution algorithm. In Proceedings of MENDEL 2003, 9th International Conference on Soft Computing, pages 11--18, Brno, Czech Republic, 4--6 June 2003. ISBN 80-214-2411-7.

Google Scholar

[12]

Y.-W. Shang and Y.-H. Qiu. A note on the extended rosenbrock function. Evolutionary Computation, 14(1):119--126, 2006.

Digital Library

Google Scholar

[13]

R. Storn and K. Price. Differential evolution -- a simple and efficient adaptive scheme for global optimization over continuos spaces. Technical Report TR-95-012, ICSI, 1995.

Google Scholar

[14]

R. Storn and K. Price. Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, pages 341--359, 1997.

Digital Library

Google Scholar

Cited By

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Burnaev EPanov M(2015)Adaptive Design of Experiments Based on Gaussian ProcessesStatistical Learning and Data Sciences10.1007/978-3-319-17091-6_7(116-125)Online publication date: 3-Apr-2015
https://doi.org/10.1007/978-3-319-17091-6_7
Epitropakis MLi XBurke E(2013)A dynamic archive niching differential evolution algorithm for multimodal optimization2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557556(79-86)Online publication date: Jun-2013
https://doi.org/10.1109/CEC.2013.6557556
Epitropakis MPlagianakos VVrahatis M(2012)Multimodal optimization using niching differential evolution with index-based neighborhoods2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6256480(1-8)Online publication date: Jun-2012
https://doi.org/10.1109/CEC.2012.6256480
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Index Terms

An extended mutation concept for the local selection based differential evolution algorithm
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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Published In

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

July 2007

2313 pages

ISBN:9781595936974

DOI:10.1145/1276958

General Chair:
Hod Lipson
Cornell University, USA

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

Publication History

Published: 07 July 2007

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GECCO07

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GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, England

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;

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

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Cited By

View all

Burnaev EPanov M(2015)Adaptive Design of Experiments Based on Gaussian ProcessesStatistical Learning and Data Sciences10.1007/978-3-319-17091-6_7(116-125)Online publication date: 3-Apr-2015
https://doi.org/10.1007/978-3-319-17091-6_7
Epitropakis MLi XBurke E(2013)A dynamic archive niching differential evolution algorithm for multimodal optimization2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557556(79-86)Online publication date: Jun-2013
https://doi.org/10.1109/CEC.2013.6557556
Epitropakis MPlagianakos VVrahatis M(2012)Multimodal optimization using niching differential evolution with index-based neighborhoods2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6256480(1-8)Online publication date: Jun-2012
https://doi.org/10.1109/CEC.2012.6256480
Epitropakis MPlagianakos VVrahatis M(2011)Finding multiple global optima exploiting differential evolution's niching capability2011 IEEE Symposium on Differential Evolution (SDE)10.1109/SDE.2011.5952058(1-8)Online publication date: Apr-2011
https://doi.org/10.1109/SDE.2011.5952058

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