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CMA-ES: Covariance Matrix Adaptation Evolution Strategy
Participant : Nikolaus Hansen [correspondent] .
Evolutionary Computation, stochastic optimization, real-parameter optimization
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is one of the most powerful continuous domain evolutionary algorithms. The CMA-ES is considered state-of-the-art in continuous domain evolutionary computation (H.-G. Beyer (2007). Evolution Strategies, Scholarpedia, page 1965.) and has been shown to be highly competitive on different problem classes. The algorithm is widely used in research and industry as witnessed by hundreds of published applications. We provide source code for the CMA-ES in C, Java, Matlab, Octave, Python, and Scilab including the latest variants of the algorithm.