Improving (1+1) Covariance Matrix Adaptation Evolution Strategy: a simple yet efficient approach
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.
The file attached to this record is the author's final peer reviewed version
Citation : Caraffini, F., Iacca, G., Yaman, A. (2018) Improving (1+1) Covariance Matrix Adaptation Evolution Strategy: a simple yet efficient approach. LeGO 2018: 14th International Workshop on Global Optimization, Leiden, The Netherlands, 18-21 September 2018.
Research Group : Institute of Artificial Intelligence (IAI)
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes