Cumulative Step-size Adaptation on Linear Functions: Technical Report
Machine Learning
2012-07-02 v2
Abstract
The CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation, where the step size is adapted measuring the length of a so-called cumulative path. The cumulative path is a combination of the previous steps realized by the algorithm, where the importance of each step decreases with time. This article studies the CSA-ES on composites of strictly increasing with affine linear functions through the investigation of its underlying Markov chains. Rigorous results on the change and the variation of the step size are derived with and without cumulation. The step-size diverges geometrically fast in most cases. Furthermore, the influence of the cumulation parameter is studied.
Keywords
Cite
@article{arxiv.1206.1208,
title = {Cumulative Step-size Adaptation on Linear Functions: Technical Report},
author = {Alexandre Adrien Chotard and Anne Auger and Nikolaus Hansen},
journal= {arXiv preprint arXiv:1206.1208},
year = {2012}
}
Comments
Parallel Problem Solving From Nature (2012)