Co-optimization Algorithms

Author

Travis Service
Daniel R. Tauritz, Missouri University of Science and TechnologyFollow

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.

Recommended Citation

T. Service and D. R. Tauritz, "Co-optimization Algorithms," Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery (ACM), Jul 2008.

The definitive version is available at https://doi.org/10.1145/1389095.1389166

Department(s)

Computer Science

Keywords and Phrases

Gradient Ascent; Algorithms; 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

01 Jul 2008