What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains?

Roberto Ugolotti, Laura Sani and Stefano Cagnoni
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Roberto Ugolotti: Camlin Italy, via Budellungo 2, 43123 Parma, Italy
Laura Sani: Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/a, 43124 Parma, Italy
Stefano Cagnoni: Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/a, 43124 Parma, Italy

Mathematics, 2019, vol. 7, issue 3, 1-25

Abstract: Properly configuring Evolutionary Algorithms (EAs) is a challenging task made difficult by many different details that affect EAs’ performance, such as the properties of the fitness function, time and computational constraints, and many others. EAs’ meta-optimization methods, in which a metaheuristic is used to tune the parameters of another (lower-level) metaheuristic which optimizes a given target function, most often rely on the optimization of a single property of the lower-level method. In this paper, we show that by using a multi-objective genetic algorithm to tune an EA, it is possible not only to find good parameter sets considering more objectives at the same time but also to derive generalizable results which can provide guidelines for designing EA-based applications. In particular, we present a general framework for multi-objective meta-optimization, to show that “going multi-objective” allows one to generate configurations that, besides optimally fitting an EA to a given problem, also perform well on previously unseen ones.

Keywords: evolutionary algorithms; multi-objective optimization; parameter puning; parameter analysis; particle swarm optimization; differential evolution; global continuous optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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