@inproceedings{d3ae70867717426ea9fae64f6b13e517,
title = "Improving (1+1) covariance matrix adaptation evolution strategy: a simple yet efficient approach",
abstract = "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.",
author = "Fabio Caraffini and Giovanni Iacca and Anil Yaman",
year = "2019",
month = feb,
day = "12",
doi = "10.1063/1.5089971",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics",
editor = "Deutz, {Andre H.} and Hille, {Sander C.} and Sergeyev, {Yaroslav D.} and Emmerich, {Michael T. M.}",
booktitle = "Proceedings LeGO 2018 : 14th International Global Optimization Workshop",
address = "United States",
note = "14th International Global Optimization Workshop, LeGO 2018 ; Conference date: 18-09-2018 Through 21-09-2018",
}