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Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Mirrored variants of the (1,2)-CMA-ES compared on the noiseless BBOB-2010 testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Mirrored variants of the (1,4)-CMA-ES compared on the noiseless BBOB-2010 testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Mirrored variants of the (1,2)-CMA-ES compared on the noisy BBOB-2010 testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Mirrored variants of the (1,4)-CMA-ES compared on the noisy BBOB-2010 testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Investigating the impact of sequential selection in the (1,2)-CMA-ES on the noisy BBOB-2010 testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Investigating the impact of sequential selection in the (1,4)-CMA-ES on the noisy BBOB-2010 testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Mirrored sampling and sequential selection for evolution strategies
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Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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This paper presents a refined single parent evolution strategy that is derandomized with mirrored sampling and/or uses sequential selection. The paper analyzes some of the elitist variants of this algorithm. We prove, on spherical functions with finite dimension, linear convergence of different strategies with scale-invariant step-size and provide expressions for the convergence rates as the expectation of some known random variables. In addition, we derive explicit asymptotic formulae for the convergence rate when the dimension of the search space goes to infinity. Convergence rates on the sphere reveal lower bounds for the convergence rate of the respective step-size adaptive strategies. We prove the surprising result that the (1+2)-ES with mirrored sampling converges at the same rate as the (1+1)-ES without and show that the tight lower bound for the (1+λ-ES with mirrored sampling and sequential selection improves by 16% over the (1+1)-ES reaching an asymptotic value of about -0.235.