Improving (1+1) covariance matrix adaptation evolution strategy: a simple yet efficient approach

Fabio Caraffini, Giovanni Iacca, Anil Yaman

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)


In recent years, part of the meta-heuristic optimisation research community has called for a simplification of the algorithmic design: indeed, while most of the state-of-The-Art algorithms are characterised by a high level of complexity, complex algorithms are hard to understand and therefore tune for specific real-world applications. Here, we follow this reductionist approach by combining straightforwardly two methods recently proposed in the literature, namely the Re-sampling Inheritance Search (RIS) and the (1+1) Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We propose an RI-(1+1)-CMA-ES algorithm that on the one hand improves upon the original (1+1)-CMA-ES, on the other it keeps the original spirit of simplicity at the basis of RIS. We show with an extensive experimental campaign that the proposed algorithm efficiently solves a large number of benchmark functions and is competitive with several modern optimisation algorithms much more complex in terms of algorithmic design.

Original languageEnglish
Title of host publicationProceedings LeGO 2018 : 14th International Global Optimization Workshop
EditorsAndre H. Deutz, Sander C. Hille, Yaroslav D. Sergeyev, Michael T. M. Emmerich
Place of PublicationMaryland
PublisherAmerican Institute of Physics
Number of pages4
ISBN (Electronic)9780735417984
Publication statusPublished - 12 Feb 2019
Event14th International Global Optimization Workshop, LeGO 2018 - Leiden, Netherlands
Duration: 18 Sept 201821 Sept 2018

Publication series

NameAIP Conference Proceedings


Conference14th International Global Optimization Workshop, LeGO 2018


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