Abstract
It is our pleasure to introduce this special issue on multitask evolutionary computation (MTEC), focusing on novel methodologies and applications of evolutionary algorithms (EAs) crafted to perform multiple search and optimization tasks jointly. EAs are population-based methods inspired by principles of natural evolution that have provided a gradient-free path to solving complex learning and optimization problems. However, unlike the natural world where evolution has engendered diverse species and produced differently skilled subpopulations, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in silico</i> EAs are typically designed to evolve a set of solutions specialized for just a single target task. This convention of problem solving in isolation tends to curtail the power of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">implicit parallelism</i> of a population. Skills evolved for a given problem instance do not naturally transfer to populations tasked to solve another. Hence, convergence rates remain restrained, even in settings where related tasks with overlapping search spaces, similar optimal solutions, or with other forms of reusable information, are routinely recurring.
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