Experience-based Optimization: A Coevolutionary Approach

TL;DR

This paper introduces LiangYi, a novel coevolutionary offline training method for solvers that simultaneously updates the solver population and training instances, leading to significantly improved performance on complex problems like TSP.

Contribution

LiangYi is a new training approach that integrates solver and instance evolution, addressing previous two-stage methods' limitations and enhancing solver performance through continuous training.

Findings

LiangYi outperforms existing training methods on TSP instances.

The method enables continuous improvement of solvers through training.

Empirical results show significant performance gains on large test sets.

Abstract

This paper studies improving solvers based on their past solving experiences, and focuses on improving solvers by offline training. Specifically, the key issues of offline training methods are discussed, and research belonging to this category but from different areas are reviewed in a unified framework. Existing training methods generally adopt a two-stage strategy in which selecting the training instances and training instances are treated in two independent phases. This paper proposes a new training method, dubbed LiangYi, which addresses these two issues simultaneously. LiangYi includes a training module for a population-based solver and an instance sampling module for updating the training instances. The idea behind LiangYi is to promote the population-based solver by training it (with the training module) to improve its performance on those instances (discovered by the sampling module) on which it performs badly, while keeping the good performances obtained by it on previous instances. An instantiation of LiangYi on the Travelling Salesman Problem is also proposed. Empirical results on a huge testing set containing 10000 instances showed LiangYi could train solvers that perform significantly better than the solvers trained by other state-of-the-art training method. Moreover, empirical investigation of the behaviours of LiangYi confirmed it was able to continuously improve the solver through training.