Discovering new robust local search algorithms with neuro-evolution

TL;DR

This paper introduces a neural network-based method to enhance local search algorithms, making them more robust and efficient for black-box optimization problems, demonstrated on NK landscape benchmarks.

Contribution

It proposes a neural network approach to improve local search decision processes, focusing on robustness to objective function transformations and efficiency across problem complexities.

Findings

Neural network-enhanced local search outperforms traditional methods on NK landscapes.

The approach maintains robustness under monotonic transformations.

Experimental results show improved problem-solving capabilities.

Abstract

This paper explores a novel approach aimed at overcoming existing challenges in the realm of local search algorithms. Our aim is to improve the decision process that takes place within a local search algorithm so as to make the best possible transitions in the neighborhood at each iteration. To improve this process, we propose to use a neural network that has the same input information as conventional local search algorithms. In this paper, which is an extension of the work presented at EvoCOP2024, we investigate different ways of representing this information so as to make the algorithm as efficient as possible but also robust to monotonic transformations of the problem objective function. To assess the efficiency of this approach, we develop an experimental setup centered around NK landscape problems, offering the flexibility to adjust problem size and ruggedness. This approach offers a promising avenue for the emergence of new local search algorithms and the improvement of their problem-solving capabilities for black-box problems. The last version of this article is published in the journal SN Computer Science (Springer).