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Co-optimization algorithms

Authors:

Travis C. Service,

Daniel R. TauritzAuthors Info & Claims

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

Pages 387 - 388

https://doi.org/10.1145/1389095.1389166

Published: 12 July 2008 Publication History

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Abstract

While coevolution has many parallels to natural evolution, methods other than those based on evolutionary principles may be used in the interactive fitness setting. In this paper we present a generalization of coevolution to co-optimization which allows arbitrary black-box function optimization techniques to be used in a coevolutionary like manner.

We find that the co-optimization versions of gradient ascent and simulated annealing are capable of outperforming the canonical coevolutionary algorithm. We also hypothesize that techniques which employ non-population based selection mechanisms are less sensitive to disengagement.

References

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John Peter Cartlidge. Rules of Engagement: Competitive Coevolutionary Dynamics in Computational Systems. PhD thesis, University of Leeds, 2004.

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Edwin D. de Jong and Jordan B. Pollack. Ideal Evaluation from Coevolution. Evolutionary Computation, 12(2):159--192, 2004.

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Daniel Hillis. Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D Nonlinear Phenomena, 42:228--234, June 1990.

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Jordan Pollack and Alan Blair. Co-evolution in the successful learning of backgammon strategy. Machine Learning, 32(3):225--240, 1998.

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Christopher Darrell Rosin. Coevolutionary Search Among Adversaries. PhD thesis, University of California -- San Diego, 1997.

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Cited By

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Popovici E(2018)Co-optimization free lunchesEvolutionary Computation10.1162/evco_a_0020826:1(145-175)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1162/evco_a_00208
Popovici E(2017)Bridging supervised learning and test-based co-optimizationThe Journal of Machine Learning Research10.5555/3122009.312204718:1(1255-1293)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.5555/3122009.3122047
Liskowski PKrawiec K(2017)Online discovery of search objectives for test-based problemsEvolutionary Computation10.1162/evco_a_0017925:3(375-406)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1162/evco_a_00179
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Index Terms

Co-optimization algorithms
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 '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

Publication History

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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Popovici E(2018)Co-optimization free lunchesEvolutionary Computation10.1162/evco_a_0020826:1(145-175)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1162/evco_a_00208
Popovici E(2017)Bridging supervised learning and test-based co-optimizationThe Journal of Machine Learning Research10.5555/3122009.312204718:1(1255-1293)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.5555/3122009.3122047
Liskowski PKrawiec K(2017)Online discovery of search objectives for test-based problemsEvolutionary Computation10.1162/evco_a_0017925:3(375-406)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1162/evco_a_00179
Popovici EBucci AWiegand RDe Jong E(2012)Coevolutionary PrinciplesHandbook of Natural Computing10.1007/978-3-540-92910-9_31(987-1033)Online publication date: 2012
https://doi.org/10.1007/978-3-540-92910-9_31
Popovici EWinston EBucci ABeyer HLangdon W(2011)On the practicality of optimal output mechanisms for co-optimization algorithmsProceedings of the 11th workshop proceedings on Foundations of genetic algorithms10.1145/1967654.1967659(43-60)Online publication date: 5-Jan-2011
https://dl.acm.org/doi/10.1145/1967654.1967659
Jaskowski WKrawiec K(2010)Coordinate System Archive for coevolutionIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5586066(1-10)Online publication date: Jul-2010
https://doi.org/10.1109/CEC.2010.5586066

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A novel multi-population coevolution immune optimization algorithm

A cooperative coevolutionary biogeography-based optimizer

How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism

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