Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy

2012/04/11 by Ilya Loshchilov, Marc Schoenauer, Loshchilov, Ilya +3 · 1 citation
Computer Science · #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #cs.NE

paper · pdf · doi:10.48550/arxiv.1204.2356

Genetic and Evolutionary Computation Conference (GECCO 2012) (2012)

arxiv created 2012/04/11 · arxiv updated 2012/04/12

Abstract

This paper presents a novel mechanism to adapt surrogate-assisted population-based algorithms. This mechanism is applied to ACM-ES, a recently proposed surrogate-assisted variant of CMA-ES. The resulting algorithm, saACM-ES, adjusts online the lifelength of the current surrogate model (the number of CMA-ES generations before learning a new surrogate) and the surrogate hyper-parameters. Both heuristics significantly improve the quality of the surrogate model, yielding a significant speed-up of saACM-ES compared to the ACM-ES and CMA-ES baselines. The empirical validation of saACM-ES on the BBOB-2012 noiseless testbed demonstrates the efficiency and the scalability w.r.t the problem dimension and the population size of the proposed approach, that reaches new best results on some of the benchmark problems.

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• CMA-ES with Radial Basis Function Surrogate for Black-Box Optimization

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