This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation and reveals local and global search properties of the evolution strategy with and without covariance matrix adaptation.Expand

This special session is devoted to the approaches, algorithms and techniques for solving real parameter single objective optimization without making use of the exact equations of the test functions.Expand

In this review, the argument starts out with large population sizes, reflecting recent extensions of the CMA algorithm, and similarities and differences to continuous Estimation of Distribution Algorithms are analyzed.Expand

A novel evolutionary optimization strategy based on the derandomized evolution strategy with covariance matrix adaptation (CMA-ES) to reduce the number of generations required for convergence to the optimum, resulting in a highly parallel algorithm which scales favorably with large numbers of processors.Expand

The IPOP-CMA-ES is evaluated on the test suit of 25 functions designed for the special session on real-parameter optimization of CEC 2005, where the population size is increased for each restart (IPOP).Expand

A new formulation for coordinate system independent adaptation of arbitrary normal mutation distributions with zero mean is presented. This enables the evolution strategy (ES) to adapt the correct… Expand

This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation, a stochastic method for real-parameter (continuous domain) optimization of non-linear, non-convex functions.Expand

In this paper the performance of the CMA evolution strategy with rank-μ-update and weighted recombination is empirically investigated on eight multimodal test functions. In particular the effect of… Expand

A single-objective, elitist, CMA-ES is introduced using plus-selection and step size control based on a success rule and a population of individuals that adapt their search strategy as in the elitists is maintained, subject to multi-objectives selection.Expand

The testbed of noise-free functions is defined and motivated, and the participants' favorite black-box real-parameter optimizer in a few dimensions a few hundreds of times and execute the provided post-processing script afterwards.Expand