Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions
This survey provides a structured overview and identifies open challenges for researchers interested in the field of Evolutionary Multitask Optimization.
This paper provides a survey of Evolutionary Multitask Optimization (EMTO), a field that aims to solve multiple optimization problems simultaneously by exploiting complementarities among them. It organizes and critically examines existing literature, focusing on methodological patterns like multifactorial optimization and multipopulation-based multitasking.
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.