Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Bi-population CMA-ES agorithms with surrogate models and line searches
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This paper focuses on the restart strategy of CMA-ES on multi-modal functions. A first alternative strategy proceeds by decreasing the initial step-size of the mutation while doubling the population size at each restart. A second strategy adaptively allocates the computational budget among the restart settings in the BIPOP scheme. Both restart strategies are validated on the BBOB benchmark; their generality is also demonstrated on an independent real-world problem suite related to spacecraft trajectory optimization.