Evolutionary Multitask Optimization: Fundamental Research Questions, Practices, and Directions for the Future

TL;DR

This paper critically examines the current state of evolutionary multitask optimization, highlighting unresolved issues in its practicality, novelty, and evaluation methods, and calls for focused future research to address these gaps.

Contribution

It provides a reflective analysis of key challenges in evolutionary multitask optimization and outlines directions for future research to improve its theoretical and practical foundations.

Findings

Identifies gaps in the practicality of the paradigm

Highlights issues with the novelty of proposed methods

Critiques current evaluation methodologies

Abstract

Transfer Optimization has gained a remarkable attention from the Swarm and Evolutionary Computation community in the recent years. It is undeniable that the concepts underlying Transfer Optimization are formulated on solid grounds. However, evidences observed in recent contributions confirm that there are critical aspects that are not properly addressed to date. This short communication aims to engage the readership around a reflection on these issues, and to provide rationale why they remain unsolved. Specifically, we emphasize on three critical points of Evolutionary Multitasking Optimization: i) the plausibility and practical applicability of this paradigm; ii) the novelty of some proposed multitasking methods; and iii) the methodologies used for evaluating newly proposed multitasking algorithms. As a result of this research, we conclude that some important efforts should be directed by the community in order to keep the future of this promising field on the right track. Our ultimate purpose is to unveil gaps in the current literature, so that prospective works can attempt to fix these gaps, avoiding to stumble on the same stones and eventually achieve valuable advances in the area.