Evolutionary computation for multicomponent problems: opportunities and future directions

TL;DR

This paper reviews the progress of evolutionary computation over 30 years in solving complex, industry-inspired problems, examining if current research aligns with the evolving challenges of real-world applications.

Contribution

It critically assesses the relevance of past evolutionary computation approaches to modern complex problems and discusses potential mismatches and future directions.

Findings

Evolutionary methods have historically targeted industry-relevant problems.

Complexity of real-world problems has increased significantly.

Potential misalignment between research focus and current industry challenges.

Abstract

Over the past 30 years many researchers in the field of evolutionary computation have put a lot of effort to introduce various approaches for solving hard problems. Most of these problems have been inspired by major industries so that solving them, by providing either optimal or near optimal solution, was of major significance. Indeed, this was a very promising trajectory as advances in these problem-solving approaches could result in adding values to major industries. In this paper we revisit this trajectory to find out whether the attempts that started three decades ago are still aligned with the same goal, as complexities of real-world problems increased significantly. We present some examples of modern real-world problems, discuss why they might be difficult to solve, and whether there is any mismatch between these examples and the problems that are investigated in the evolutionary computation area.