AAAI Publications, Twenty-Sixth AAAI Conference on Artificial Intelligence

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Improving Convergence of CMA-ES Through Structure-Driven Discrete Recombination
Tim Brys, Ann Nowé

Last modified: 2012-07-14

Abstract


Evolutionary Strategies (ES) are a class of continuous optimization algorithms that have proven to perform very well on hard optimization problems. Whereas in earlier literature, both intermediate and discrete recombination operators were used, we now see that most ES, e.g. CMA-ES, use only intermediate recombination. While CMA-ES is considered state-of-the-art in continuous optimization, we believe that reintroducing discrete recombination can improve the algorithms' ability to escape local optima. Specifically, we look at using information on the problem's structure to create building blocks for recombination.

Keywords


CMA-ES; Evolution Strategies; Problem Structure

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