Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration

TL;DR

This paper enhances Nevergrad's NGOpt algorithm selection wizard by applying automated configuration with irace to optimize its constituent algorithms, leading to improved performance across diverse benchmarks.

Contribution

It introduces automated configuration using irace to optimize NGOpt's algorithm configurations, replacing hand-crafted settings for better adaptability and performance.

Findings

Improved NGOpt performance on multiple benchmarks

Automated configuration outperforms manual tuning

Enhancement generalizes to unseen problem suites

Abstract

Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc. State-of-the-art algorithm selection wizards are complex and difficult to improve. We propose in this work the use of automated configuration methods for improving their performance by finding better configurations of the algorithms that compose them. In particular, we use elitist iterated racing (irace) to find CMA configurations for specific artificial benchmarks that replace the hand-crafted CMA configurations currently used in the NGOpt wizard provided by the Nevergrad platform. We discuss in detail the setup of irace for the purpose of generating configurations that work well over the diverse set of problem instances within each benchmark. Our approach improves the performance of the NGOpt wizard, even on benchmark suites that were not part of the tuning by irace.