A parallel implementation of the covariance matrix adaptation evolution strategy
Neural and Evolutionary Computing
2018-05-30 v1 Optimization and Control
Abstract
In many practical optimization problems, the derivatives of the functions to be optimized are unavailable or unreliable. Such optimization problems are solved using derivative-free optimization techniques. One of the state-of-the-art techniques for derivative-free optimization is the covariance matrix adaptation evolution strategy (CMA-ES) algorithm. However, the complexity of CMA-ES algorithm makes it undesirable for tasks where fast optimization is needed. To reduce the execution time of CMA-ES, a parallel implementation is proposed, and its performance is analyzed using the benchmark problems in PythOPT optimization environment.
Keywords
Cite
@article{arxiv.1805.11201,
title = {A parallel implementation of the covariance matrix adaptation evolution strategy},
author = {Najeeb Khan},
journal= {arXiv preprint arXiv:1805.11201},
year = {2018}
}