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Article
Co-optimization Algorithms
Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation
  • Travis Service
  • Daniel R. Tauritz, Missouri University of Science and Technology
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.

Department(s)
Computer Science
Keywords and Phrases
  • Gradient Ascent,
  • Algorithms
Library of Congress Subject Headings
Coevolution
Simulated annealing (Mathematics)
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2008 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
7-1-2008
Disciplines
Citation Information
Travis Service and Daniel R. Tauritz. "Co-optimization Algorithms" Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (2008)
Available at: http://works.bepress.com/daniel-tauritz/22/