A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization

TL;DR

This survey reviews recent advancements in learnable multiobjective evolutionary algorithms (MOEAs) that incorporate machine learning techniques to address the challenges of scaling-up multiobjective optimization problems with increased complexity.

Contribution

It provides a comprehensive taxonomy and analysis of recent learnable MOEAs, highlighting four key directions for improving scalability and effectiveness in complex MOPs.

Findings

Learnable MOEAs address challenges of large-scale, multiobjective problems.

Four main directions: discriminators, generators, evaluators, transfer modules.

Recent advances improve scalability and efficiency of MOEAs.

Abstract

Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand challenges brought by the scaling-up MOPs with continuously increasing complexity from diverse aspects, mainly including expensive cost of function evaluations, many objectives, large-scale search space, time-varying environments, and multi-task. Under different scenarios, divergent thinking is required in designing new powerful MOEAs for solving them effectively. In this context, research studies on learnable MOEAs with machine learning techniques have received extensive attention in the field of evolutionary computation. This paper begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs. Then, we synthetically overview recent advances of learnable MOEAs in solving various scaling-up MOPs, focusing primarily on four attractive directions (i.e., learnable evolutionary discriminators for environmental selection, learnable evolutionary generators for reproduction, learnable evolutionary evaluators for function evaluations, and learnable evolutionary transfer modules for sharing or reusing optimization experience). The insight of learnable MOEAs is offered to readers as a reference to the general track of the efforts in this field.