Please use this identifier to cite or link to this item:

TitleDeriving and improving CMA-ES with information geometric trust regions
Author(s)Abdolmaleki, Abbas
Price, Bob
Lau, Nuno
Reis, L. P.
Neumann, Gerhard
KeywordsExpectation maximisation
Stochastic search
Trust regions
Issue date1-Jul-2017
PublisherAssociation for Computing Machinery (ACM)
CitationAbdolmaleki, A., Price, B., Lau, N., Reis, L. P., & Neumann, G. (2017, July). Deriving and improving CMA-ES with information geometric trust regions. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 657-664). ACM
Abstract(s)CMA-ES is one of the most popular stochastic search algorithms. It performs favourably in many tasks without the need of extensive parameter tuning. The algorithm has many beneficial properties, including automatic step-size adaptation, efficient covariance updates that incorporates the current samples as well as the evolution path and its invariance properties. Its update rules are composed of well established heuristics where the theoretical foundations of some of these rules are also well understood. In this paper we will fully derive all CMA-ES update rules within the framework of expectation-maximisation-based stochastic search algorithms using information-geometric trust regions. We show that the use of the trust region results in similar updates to CMA-ES for the mean and the covariance matrix while it allows for the derivation of an improved update rule for the step-size. Our new algorithm, Trust-Region Co-variance Matrix Adaptation Evolution Strategy (TR-CMA-ES) is fully derived from first order optimization principles and performs favourably in compare to standard CMA-ES algorithm.
TypeConference paper
Publisher version
AccessOpen access
Appears in Collections:DSI - Engenharia da Programação e dos Sistemas Informáticos

Files in This Item:
File Description SizeFormat 
2017_GECCO_Deriving and Improving CMA-ES_Abdolmaleki_Lau_Reis.pdf2,14 MBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID