An Asynchronous Implementation of the Limited Memory CMA-ES

TL;DR

This paper introduces an asynchronous version of the LM-CMA-ES algorithm, enhancing its performance on large-scale optimization problems especially with high core counts and complex fitness functions.

Contribution

It presents a novel asynchronous implementation of LM-CMA-ES that improves efficiency and performance on benchmark functions compared to the original version.

Findings

Outperforms original on Rastrigin function

Faster convergence on Rosenbrock and Ellipsoid functions

Effective with high core counts and complex fitness evaluations

Abstract

We present our asynchronous implementation of the LM-CMA-ES algorithm, which is a modern evolution strategy for solving complex large-scale continuous optimization problems. Our implementation brings the best results when the number of cores is relatively high and the computational complexity of the fitness function is also high. The experiments with benchmark functions show that it is able to overcome its origin on the Sphere function, reaches certain thresholds faster on the Rosenbrock and Ellipsoid function, and surprisingly performs much better than the original version on the Rastrigin function.