Adaptive global optimization with local search
Adaptive global optimization with local search
Memetic algorithms: a short introduction
New ideas in optimization
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Evolutionary algorithms with local search for combinatorial optimization
Evolutionary algorithms with local search for combinatorial optimization
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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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.