Deriving and improving CMA-ES with information geometric trust regions

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Abdolmaleki, A (Author) Other; Price, B (Author) Other; lau, n (Author) FCUP; reis, lp (Author) REIT; Neumann, G (Author) Other

Conference proceedings International

Title: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, Berlin, Germany, July 15-19, 2017

Pages: 657-664

Indexing

Scopus - 11 Citations

Other information

Authenticus ID: P-00M-YKA

DOI: 10.1145/3071178.3071252

Abstract (EN): 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. © 2017 ACM.

Language: English

Type (Professor's evaluation): Scientific