Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Software and Platforms

CMA-ES: Covariance Matrix Adaptation Evolution Strategy

Participants : Emmanuel Benazera, Nikolaus Hansen [correspondent] .

Keywords: Evolutionary Computation, Stochastic Optimization, Real-parameter Optimization.

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [65] is considered to be state-of-the-art in continuous domain evolutionary computation [64] , and in stochastic optimization at large. It has been shown to be highly competitive on different problem classes even with deterministic continuous algorithms using numerically computed gradients (see the results published on COCO platform). 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, C++11, Java, Matlab, Octave, Python, and Scilab including the latest variants of the algorithm.