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Conference Papers Year : 2016

Parameter Setting for Multicore CMA-ES with Large Populations

Abstract

The goal of this paper is to investigate on the overall performance of CMA-ES, when dealing with a large number of cores — considering the direct mapping between cores and individuals — and to empirically find the best parameter strategies for a parallel machine. By considering the problem of parameter setting, we empirically determine a new strategy for CMA-ES, and we investigate whether Self-CMA-ES (a self-adaptive variant of CMA-ES) could be a viable alternative to CMA-ES when using parallel computers with a coarse-grained distribution of the fitness evaluations. According to a large population size, the resulting new strategy for Self-CMA-ES and CMA-ES, is experimentally validated on BBOB benchmark where it is shown to outperform a CMA-ES with default parameter strategy.
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Dates and versions

hal-01236025 , version 1 (01-12-2015)

Identifiers

  • HAL Id : hal-01236025 , version 1

Cite

Nacim Belkhir, Johann Dréo, Pierre Savéant, Marc Schoenauer. Parameter Setting for Multicore CMA-ES with Large Populations. Artificial Evolution (EA 2015), Oct 2015, Lyon, France. pp.109-122. ⟨hal-01236025⟩
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