Preselection via Classification: A Case Study on Evolutionary Multiobjective Optimization

TL;DR

This paper introduces a classification-based preselection method for evolutionary multiobjective optimization that improves algorithm performance by filtering promising solutions without explicit objective value estimation.

Contribution

It proposes a novel CPS strategy that uses classification to select promising offspring, reducing the need for objective evaluations in MOEAs.

Findings

CPS improves performance of state-of-the-art MOEAs

Reduces computational cost by avoiding objective evaluations

Effective across multiple test instances

Abstract

In evolutionary algorithms, a preselection operator aims to select the promising offspring solutions from a candidate offspring set. It is usually based on the estimated or real objective values of the candidate offspring solutions. In a sense, the preselection can be treated as a classification procedure, which classifies the candidate offspring solutions into promising ones and unpromising ones. Following this idea, we propose a classification based preselection (CPS) strategy for evolutionary multiobjective optimization. When applying classification based preselection, an evolutionary algorithm maintains two external populations (training data set) that consist of some selected good and bad solutions found so far; then it trains a classifier based on the training data set in each generation. Finally it uses the classifier to filter the unpromising candidate offspring solutions and choose a promising one from the generated candidate offspring set for each parent solution. In such cases, it is not necessary to estimate or evaluate the objective values of the candidate offspring solutions. The classification based preselection is applied to three state-of-the-art multiobjective evolutionary algorithms (MOEAs) and is empirically studied on two sets of test instances. The experimental results suggest that classification based preselection can successfully improve the performance of these MOEAs.