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Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference - GECCO '09 - A memetic algorithm using local search chaining forblack-box optimization benchmarking 2009 for noise free functions by Molina, Daniel; Lozano, Manuel; Herrera, Francisco is a scholarly article available to read on EtoBox.

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Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous search algorithms that stand out as brilliant local search optimisers. Several of them, like CMA-ES, often require a high number of evaluations to adapt its parameters. Unfortunately, this feature makes difficult to use them to create memetic algorithms. In this work, we show a memetic algorithm that applies CMA-ES to refine the solutions, assigning to each individual a local search intensity that depends on its features, by chaining different local search applications. Experiments are carried out on the noise free Black-Box Optimization Benchmarking BBOB'2009 test suite.

Author

Molina, Daniel; Lozano, Manuel; Herrera, Francisco

Publisher

ACM Press

Published

2009

Language

EN