Co-optimization AlgorithmsProceedings of the 10th Annual Conference on Genetic and Evolutionary Computation
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.
Keywords and Phrases
- Gradient Ascent,
- Simulated annealing (Mathematics)
Document TypeArticle - Conference proceedings
Rights© 2008 Association for Computing Machinery (ACM), All rights reserved.
Citation InformationTravis 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/