Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions

TL;DR

This survey reviews the development of evolutionary multitask optimization, highlighting methodological patterns, challenges, and future research directions in leveraging evolutionary computation for solving multiple problems simultaneously.

Contribution

It provides a comprehensive overview of the field, critically analyzing existing methods like multifactorial optimization and identifying open challenges and promising future research avenues.

Findings

Critical analysis of existing evolutionary multitasking methods

Identification of key challenges in the field

Proposed future research directions

Abstract

In this work we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The principal goal when dealing with this scenario is to dynamically exploit the existing complementarities among the problems (tasks) being optimized, helping each other through the exchange of valuable knowledge. Additionally, the emerging paradigm of Evolutionary Multitasking tackles multitask optimization scenarios by using as inspiration concepts drawn from Evolutionary Computation. The main purpose of this survey is to collect, organize and critically examine the abundant literature published so far in Evolutionary Multitasking, with an emphasis on the methodological patterns followed when designing new algorithmic proposals in this area (namely, multifactorial optimization and multipopulation-based multitasking). We complement our critical analysis with an identification of challenges that remain open to date, along with promising research directions that can stimulate future efforts in this topic. Our discussions held throughout this manuscript are offered to the audience as a reference of the general trajectory followed by the community working in this field in recent times, as well as a self-contained entry point for newcomers and researchers interested to join this exciting research avenue.