The CMA Evolution Strategy: A Tutorial

TL;DR

This tutorial explains the CMA Evolution Strategy, a stochastic optimization method for continuous, non-linear, non-convex functions, detailing its derivation from intuitive concepts and optimization requirements.

Contribution

It provides a comprehensive, accessible introduction and derivation of the CMA-ES algorithm for continuous optimization problems.

Findings

CMA-ES effectively optimizes complex non-linear functions.

The tutorial clarifies the intuitive basis of the algorithm.

It demonstrates the algorithm's applicability to real-world optimization tasks.

Abstract

This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized, method for real-parameter (continuous domain) optimization of non-linear, non-convex functions. We try to motivate and derive the algorithm from intuitive concepts and from requirements of non-linear, non-convex search in continuous domain.