a memetic algorithm using local search chaining for black-box optimization benchmarking 2009 for noisy functions
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 noisy Black-Box Optimization Benchmarking BBOB'2009 test suite.
memetic algorithms black-box optimization evolutionary computation hybrid metaheuristics