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Title: Co-optimization algorithms
Author (s): Service, Travis C.
Tauritz, Daniel R.
Department/Lab Affiliations: Computer Science
Energy Research and Development Center
Intelligent Systems Center
Keywords: Gradient ascent
algorithms
Subject Terms: Coevolution.
Simulated annealing (Mathematics)
Issue Date: 2008-07
Publisher: Association for Computing Machinery
Citation: Service, Travis C., and Daniel R. Tauritz. Co-optimization algorithms, Proceedings of the 10th annual conference on Genetic and evolutionary computation (July 2008): 387-388.
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.
Type: Article - Conference proceedings
text
In Title: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation
Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Pre-print: author can archive; Post-print: author can archive;
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Publisher URL:
http://doi.acm.org/10.1145/1389095.1389166
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titleCo-optimization algorithms
contributor.authorService, Travis C.
contributor.authorTauritz, Daniel R.
contributor.deptlabComputer Science
contributor.deptlabEnergy Research and Development Center
contributor.deptlabIntelligent Systems Center
subjectGradient ascent
subjectalgorithms
subject.LCSHCoevolution.
subject.LCSHSimulated annealing (Mathematics)
date.issued2008-07
publisherAssociation for Computing Machinery
identifier.citationService, Travis C., and Daniel R. Tauritz. Co-optimization algorithms, Proceedings of the 10th annual conference on Genetic and evolutionary computation (July 2008): 387-388.
identifier.pub.URI
http://doi.acm.org/10.1145/1389095.1389166
description.abstractWhile 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.
typeArticle - Conference proceedings
type.DCMITypetext
type.statusPostprint
relation.isPartOfProceedings of the 10th Annual Conference on Genetic and Evolutionary Computation
rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
rightsPre-print: author can archive; Post-print: author can archive;
rights.URI
http://www.acm.org/pubs/copyright_policy/
date.available2008-11-20T22:37:57Z
identifier.persist.URI
http://scholarsmine.mst.edu/post_prints/Co-OptimizationAlgorithms_09007dcc805a9536.html