CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is a stochastic, derivative-free methods for numerical optimization of non-linear or non-convex continuous optimization problems. More...

## Classes | |

class | edoEstimatorNormalAdaptive< EOT, D > |

An estimator that works on adaptive normal distributions, basically the heart of the CMA-ES algorithm. More... | |

class | edoNormalAdaptive< EOT > |

A normal distribution that can be updated via several components. More... | |

class | edoSamplerNormalAdaptive< EOT, D > |

Sample points in a multi-normal law defined by a mean vector, a covariance matrix, a sigma scale factor and evolution paths. More... |

CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is a stochastic, derivative-free methods for numerical optimization of non-linear or non-convex continuous optimization problems.