Article

Exploring the behavior of building blocks for multi-objective variation operator design using predator-prey dynamics

Authors:

Christian Grimme,

Joachim Lepping,

Alexander PapaspyrouAuthors Info & Claims

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

Pages 805 - 812

https://doi.org/10.1145/1276958.1277119

Published: 07 July 2007 Publication History

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

Exploring the behavior of building blocks for multi-objective variation operator design using predator-prey dynamics

Pages 805 - 812

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Abstract

In this paper, we utilize a predator-prey model in order to identify characteristics of single-objective variation operators in the multi-objective problem domain. In detail, we analyze exemplarily Gaussian mutation and simplex recombination to find explanations for the observed behaviorswithin this model. Then, both operators are combinedto a new complex one for the multi-objective case in order to aggregate the identified properties. Finally, we show that (a) characteristic properties can still be observed in the combination and (b) the collaboration of those operators is beneficial for solving an exemplary multi-objective problem regarding convergence and diversity.

References

[1]

C. C. Coello, D. van Veldhuizen, and G. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, NY, 2002.

Digital Library

Google Scholar

[2]

C. Grimme and J. Lepping. Designing Multi-Objective Variation Operators Using a Predator-Prey Approach. In S. Obayashi et al., editors, Proceeding of the Conference on Evolutionary Multi-Criterion Optimization, volume 4403 of LNCS, pages 21--35. Springer, 2007.

Digital Library

Google Scholar

[3]

C. Grimme and K. Schmitt. Inside a Predator-Prey Model for Multi-Objective Optimization: A Second Study. In H.-G. Beyer et al., editors, Genetic and Evolutionary Computation Conference, pages 707--714, New York, 2006. ACM Press.

Digital Library

Google Scholar

[4]

T. Hanne. On the convergence of multiobjective evolutionary algorithms. European Journal of Operational Research, 117:553--564, 1999.

Crossref

Google Scholar

[5]

M. Kirley and R. Stuart. Multiobjective Evolutionary Algorithms on Complex Networks. In S. Obayashi et al., editors, Proceeding of the Conference on Evolutionary Multi-Criterion Optimization, volume 4403 of LNCS, pages 81--95. Springer, 2007.

Digital Library

Google Scholar

[6]

F. Kursawe. A Variant of Evolution Strategies for Vector Optimization. In H.-P. Schwefel and R. Männer, editors, Parallel Problem Solving from Nature I, volume 496 of LNCS, pages 193--197. Springer, 1991.

Digital Library

Google Scholar

[7]

M. Laumanns, G. Rudolph, and H.-P. Schwefel. A Spatial Predator-Prey Approach to Multi-Objective Optimization: A Preliminary Study. In T. Bäck et al., editors, Parallel Problem Solving From Nature V, pages 241--249. Springer, 1998.

Digital Library

Google Scholar

[8]

M. Laumanns, L. Thiele, K. Deb, and E. Zitzler. Combining Convergence and Diversity in Multiobjective Optimization. Evolutionary Computation, 10(3):263--282, March 2002.

Digital Library

Google Scholar

[9]

H. J. Nebro, J. J. Durillo, F. Luna, B. Dorronsoro, and E. Alba. Design Issues in a Multiobjective Cellular Genetic Algorithm. In S. Obayashi et al., editors, Proceeding of the Conference on Evolutionary Multi-Criterion Optimization, volume 4403 of LNCS, pages 126--140. Springer, 2007.

Digital Library

Google Scholar

[10]

O. Rudenko and M. Schoenauer. Dominance based crossover operator for evolutionary multi-objective algorithms. In X. Yao et al., editors, Parallel Problem Solving from Nature VIII, volume 3242 of LNCS, pages 812--821. Springer, 2004.

Google Scholar

[11]

G. Rudolph. On a Multi-Objective Evolutionary Algorithm and Its Convergence to the Pareto Set. In Proc. of the 5th IEEE CEC, pages 511--516. IEEE Press, 1998.

Crossref

Google Scholar

[12]

T. P. Runarsson. An Asynchronous Parallel Evolution Strategy. Journal of Computational Intelligence and Applications, 3(4):1--14, 2003.

Google Scholar

[13]

H.-P. Schwefel and G. Rudolph. Contemporary evolution strategies. In F. Morán et al., editors, Advances in Artificial Life -- Third European Conference on Artificial Life, pages 893--907. Springer, 1995.

Digital Library

Google Scholar

[14]

E. Zitzler and L. Thiele. Multiobjective Optimization Using Evolutionary Algorithms -- A Comparative Case Study. In Parallel Problem Solving from Nature V, volume 1498 of LNCS, pages 292--304. Springer, 1998.

Digital Library

Google Scholar

Cited By

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Grimme CLepping JPapaspyrou A(2018)Parallel predator---prey interaction for evolutionary multi-objective optimizationNatural Computing: an international journal10.1007/s11047-011-9266-911:3(519-533)Online publication date: 20-Dec-2018
https://dl.acm.org/doi/10.1007/s11047-011-9266-9
Fölling AGrimme CLepping JPapaspyrou A(2018)Connecting Community-Grids by supporting job negotiation with coevolutionary Fuzzy-SystemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-010-0667-y15:12(2375-2387)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1007/s00500-010-0667-y
Grimme CKemmerling MLepping J(2013)On the Integration of Theoretical Single-Objective Scheduling Results for Multi-objective ProblemsEVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation10.1007/978-3-642-32726-1_10(333-363)Online publication date: 2013
https://doi.org/10.1007/978-3-642-32726-1_10
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Exploring the behavior of building blocks for multi-objective variation operator design using predator-prey dynamics

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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

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Published: 07 July 2007

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July 7 - 11, 2007

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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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Grimme CLepping JPapaspyrou A(2018)Parallel predator---prey interaction for evolutionary multi-objective optimizationNatural Computing: an international journal10.1007/s11047-011-9266-911:3(519-533)Online publication date: 20-Dec-2018
https://dl.acm.org/doi/10.1007/s11047-011-9266-9
Fölling AGrimme CLepping JPapaspyrou A(2018)Connecting Community-Grids by supporting job negotiation with coevolutionary Fuzzy-SystemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-010-0667-y15:12(2375-2387)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1007/s00500-010-0667-y
Grimme CKemmerling MLepping J(2013)On the Integration of Theoretical Single-Objective Scheduling Results for Multi-objective ProblemsEVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation10.1007/978-3-642-32726-1_10(333-363)Online publication date: 2013
https://doi.org/10.1007/978-3-642-32726-1_10
Grimme CLepping JLanzi P(2011)Integrating niching into the predator-prey model using epsilon-constraintsProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2001920(109-110)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001858.2001920
Grimme CKemmerling MLepping JLanzi P(2011)An expertise-guided multi-criteria approach to scheduling problemsProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2001887(47-48)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001858.2001887
Grimme CLepping J(2011)Combining basic heuristics for solving multi-objective scheduling problems2011 IEEE Symposium on Computational Intelligence in Scheduling (SCIS)10.1109/SCIS.2011.5976543(9-16)Online publication date: Apr-2011
https://doi.org/10.1109/SCIS.2011.5976543
Fölling AGrimme CLepping JPapaspyrou ASchwiegelshohn U(2009)Competitive coevolutionary learning of fuzzy systems for job exchange in computational gridsEvolutionary Computation10.1162/evco.2009.17.4.1740617:4(545-560)Online publication date: 1-Dec-2009
https://dl.acm.org/doi/10.1162/evco.2009.17.4.17406
Grimme CLepping JPapaspyrou A(2008)The Parallel Predator-Prey ModelProceedings of the 10th International Conference on Parallel Problem Solving from Nature --- PPSN X - Volume 519910.5555/2951659.2951731(681-690)Online publication date: 13-Sep-2008
https://dl.acm.org/doi/10.5555/2951659.2951731

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Designing multi-objective variation operators using a predator-prey approach

Inside a predator-prey model for multi-objective optimization: a second study

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