About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
GEC 2009
Conference paper
Embedded self-adaptation to escape from local optima
Abstract
Self-adaptation in genetic algorithms has been suggested as a strategy to enhance evolutionary algorithms for optimization tasks. We consider continuous self-adaptation schemes called strategies that are governed by evolutionary rules, and suggest to incorporate multiple strategies to improve the performance of genetic algorithms. We show that employing multiple strategies, and letting evolutionary pressure steer adaptation, can overcome the problem of premature convergence. To demonstrate the power of our method we apply it to optimization problems of uncapacitated facility location. The method outperforms both methods with a single strategy and previously reported methods on several benchmarks. In these runs, algorithms that incorporate multiple strategies avoid getting stuck in local optimum, and converge to better solutions. Copyright 2009 ACM.