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CMA-ES

Evolutionary.CMAES — Type

Covariance Matrix Adaptation Evolution Strategy Implementation: (μ/μ_I,λ)-CMA-ES

The constructor takes following keyword arguments:

  • μ is the number of parents
  • λ is the number of offspring
  • τ is a time constant for a derection vector s
  • τ_c is a time constant for a covariance matrix C
  • τ_σ is a time constant for a global step size σ
source

Description

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a stochastic derivative-free numerical optimization algorithm for difficult (non-convex, ill-conditioned, multi-modal, rugged, noisy) optimization problems in continuous search spaces [1].

The current CMA-ES algorithm implementation based on a simplified outline[2].

References