Mirrored Orthogonal Sampling for Covariance Matrix Adaptation Evolution Strategies

Evolutionary Computation
Hao WangThomas Bäck

Abstract

Generating more evenly distributed samples in high dimensional search spaces is the major purpose of the recently proposed mirrored sampling technique for evolution strategies. The diversity of the mutation samples is enlarged and the convergence rate is therefore improved by the mirrored sampling. Motivated by the mirrored sampling technique, this paper introduces a new derandomized sampling technique called mir-rored orthogonal sampling. The performance of this new technique is both theoretically analyzed and empirically studied on the sphere function. In particular, the mirrored orthogonal sampling technique is applied to the well-known Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The resulting algorithm is experimentally tested on the well-known Black-Box Optimization Benchmark (BBOB). By comparing the results from the benchmark, mirrored orthogonal sampling is found to outperform both the standard CMA-ES and its variant using mirrored sampling.

References

May 31, 2001·Evolutionary Computation·N Hansen, A Ostermeier
Jun 14, 2003·Evolutionary Computation·Nikolaus HansenPetros Koumoutsakos

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