research-article

Experimental Investigation of Recombination Operators for Differential Evolution

Authors:

Felipe Campelo,

Moisés BotelhoAuthors Info & Claims

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

Pages 221 - 228

https://doi.org/10.1145/2908812.2908852

Published: 20 July 2016 Publication History

Abstract

This paper presents a systematic investigation of the effects of sixteen recombination operators for real-coded spaces on the performance of Differential Evolution. A unified description of the operators in terms of mathematical operations of vectors is presented, and a standardized implementation is provided in the form of an R package. The objectives are to simplify the examination of similarities and differences between operators as well as the understanding of their effects on the population, and to provide a platform in which future operators can be incorporated and evaluated. An experimental comparison of the recombination operators is conducted using twenty-eight test problems, and the results are used to discuss possibly promising directions in the development of improved operators for differential evolution.

References

[1]

Y. Benjamini and D. Yekutieli. The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29:1165--1188, 2001.

[2]

M. Birattari. Tuning Metaheuristics: A Machine Learning Perspective. Springer, 2009.

Digital Library

[3]

J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation, 10(6):646--657, 2006.

Digital Library

[4]

F. Campelo and M. Botelho. ExpDE: Modular Differential Evolution for Experimenting with Operators, 2016. R package v. 0.1.2; http://cran.r-project.org/web/packages/ExpDE.

[5]

W. Conover. Practical Nonparametric Statistics. Wiley, 3rd ed. edition, 1999.

[6]

K. Deb and R. B. Agrawal. Simulated binary crossover for continuous search space. Complex Systems, 9(3):1--15, 1994.

[7]

B. Efron and R. Tibshirani. An Introduction to the Bootstrap. Chapman & Hall/CRC, 1993.

[8]

L. J. Eshelman, R. A. Caruana, and J. D. Schaffer. Biases in the crossover landscape. In Proceedings of the Third International Conference on Genetic Algorithms, pages 10--19, San Francisco, CA, USA, 1989. Morgan Kaufmann Publishers Inc.

Digital Library

[9]

L. J. Eshelman and J. D. Schaffer. Real-Coded Genetic Algorithms and Interval-Schemata. In Foundations of Genetic Algorithms, pages 187--202, 1992.

[10]

F. Goulart, F. Campelo, and J. Ramírez. Estudo dos mecanismos de funcionamento do algoritmo de evoluçao diferencial. In Anais do X Congresso Brasileiro de Inteligência Computacional, 2011. CD-ROM, paper ST26:2. In Portuguese.

[11]

S.-M. Guo and C.-C. Yang. Enhancing differential evolution utilizing eigenvector-based crossover operator. IEEE Transactions on Evolutionary Computation, 19(1):31--49, 2015.

Digital Library

[12]

T. D. Gwiazda. Crossover for single-objective numerical optimization problems, volume 1. Tomasz Gwiazda (ed.), 2006.

[13]

F. Herrera, M. Lozano, and A. Sánchez. A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study. International Journal of Intelligent Systems, 18(3):309--338, 2003.

[14]

F. Herrera, M. Lozano, and J. L. Verdegay. Tuning fuzzy logic controllers by genetic algorithms. International Journal of Approximate Reasoning, 12(3):299--315, 1995.

[15]

J. H. Holland. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Bradford book. MIT Press, 1992.

Digital Library

[16]

S. M. Islam, S. Das, S. Ghosh, S. Roy, and P. N. Suganthan. An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42(2):482--500, 2012.

Digital Library

[17]

Y.-l. Li, J. Zhang, and W.-n. Chen. Differential evolution algorithm with pca-based crossover. In Proceedings of the 14th annual conference companion on Genetic and evolutionary computation, pages 1509--1510. ACM, 2012.

Digital Library

[18]

J. J. Liang, B.-Y. Qu, P. N. Suganthan, and A. G. Hernández-Díaz. Problem definitions and evaluation criteria for the cec2013 special session on real-parameter optimization. Technical report, 2013. Online: http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2013/CEC2013.htm.

[19]

M. López-Ibánez, J. Dubois-Lacoste, T. Stützle, and M. Birattari. The irace package, iterated race for automatic algorithm configuration. Technical Report TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Belgium, 2011.

[20]

Z. Michalewicz and M. Schoenauer. Evolutionary algorithms for constrained parameter optimization problems. Evolutionary computation, 4(1):1--32, 1996.

Digital Library

[21]

D. C. Montgomery. Design and Analysis of Experiments. Wiley, 8 edition, 2012.

[22]

K. Price, R. M. Storn, and J. A. Lampinen. Differential evolution: a practical approach to global optimization. Springer, 2006.

Digital Library

[23]

A. K. Qin and P. N. Suganthan. Self-adaptive differential evolution algorithm for numerical optimization. In Proceedings of the 2005 IEEE Congress on Evolutionary Computation, volume 2, pages 1785--1791, 2005.

[24]

N. J. Radcliffe. Equivalence class analysis of genetic algorithms. Complex systems, 5(2):183--205, 1991.

[25]

D. Schlierkamp-Voosen and H. Mühlenbein. Strategy adaptation by competing subpopulations. In Parallel Problem Solving from Nature - PPSN III, pages 199--208. Springer, 1994.

Digital Library

[26]

K. Sörensen. Metaheuristics - the metaphor exposed. International Transactions in Operational Research, 22(1):3--18, 2015.

[27]

R. Storn and K. Price. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4):341--359, 1997.

Digital Library

[28]

J. Swan, S. Adriaensen, M. Bishr, E. K. Burke, J. A. Clark, P. De Causmaecker, J. Durillo, K. Hammond, E. Hart, C. G. Johnson, et al. A research agenda for metaheuristic standardization. In Proceedings of the XI Metaheuristics International Conference, pages id--1--3, 2015.

[29]

G. Syswerda. A study of reproduction in generational and steady state genetic algorithms. Foundations of genetic algorithms, 2:94--101, 1991.

[30]

A. H. Wright. Genetic algorithms for real parameter optimization. Foundations of genetic algorithms, 1:205--218, 1991.

[31]

D. Zaharie. Critical values for the control parameters of differential evolution algorithms. In Proceedings of the 8th International Conference on Soft Computing (MENDEL)., 2002.

[32]

D. Zaharie. A comparative analysis of crossover variants in differential evolution. Proceedings of IMCSIT, pages 171--181, 2007.

[33]

M. Zambrano-Bigiarini and Y. Gonzalez-Fernandez. cec2013: Benchmark Functions for the Special Session and Competition on Real-Parameter Single Objective Optimization at CEC-2013, 2015. R package v. 0.1--5.

Cited By

Coello Coello CHernández Gómez RMiguel Antonio L(2018)Fundamentals of Evolutionary Optimization: Single‐ and Multiobjective ProblemsWiley Encyclopedia of Electrical and Electronics Engineering10.1002/047134608X.W8369(1-16)Online publication date: 14-May-2018
https://doi.org/10.1002/047134608X.W8369
Mahdavi SRahnamayan SKaria C(2017)Analyzing effects of ordering vectors in mutation schemes on performance of Differential Evolution2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969582(2290-2298)Online publication date: Jun-2017
https://doi.org/10.1109/CEC.2017.7969582

Index Terms

Experimental Investigation of Recombination Operators for Differential Evolution
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Continuous space search
      2. Randomized search

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Published In

cover image ACM Conferences

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

July 2016

1196 pages

ISBN:9781450342063

DOI:10.1145/2908812

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

Copyright © 2016 ACM.

© 2016 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2016

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Conselho Nacional de Desenvolvimento Científico e Tecnológico

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GECCO '16

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SIGEVO

GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Paper Acceptance Rate 137 of 381 submissions, 36%;

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

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Cited By

Coello Coello CHernández Gómez RMiguel Antonio L(2018)Fundamentals of Evolutionary Optimization: Single‐ and Multiobjective ProblemsWiley Encyclopedia of Electrical and Electronics Engineering10.1002/047134608X.W8369(1-16)Online publication date: 14-May-2018
https://doi.org/10.1002/047134608X.W8369
Mahdavi SRahnamayan SKaria C(2017)Analyzing effects of ordering vectors in mutation schemes on performance of Differential Evolution2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969582(2290-2298)Online publication date: Jun-2017
https://doi.org/10.1109/CEC.2017.7969582

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