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

Fabio Caraffini, Giovanni Iacca, Anil Yaman

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

5 Citaten (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.

Originele taal-2Engels
TitelProceedings LeGO 2018 : 14th International Global Optimization Workshop
RedacteurenAndre H. Deutz, Sander C. Hille, Yaroslav D. Sergeyev, Michael T. M. Emmerich
Plaats van productieMaryland
UitgeverijAmerican Institute of Physics
Aantal pagina's4
ISBN van elektronische versie9780735417984
StatusGepubliceerd - 12 feb. 2019
Evenement14th International Global Optimization Workshop, LeGO 2018 - Leiden, Nederland
Duur: 18 sep. 201821 sep. 2018

Publicatie series

NaamAIP Conference Proceedings


Congres14th International Global Optimization Workshop, LeGO 2018


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