Analysis of evolutionary algorithms using multi-objective parameter tuning
Roberto Ugolotti, Stefano Cagnoni · 2014
Evolutionary Algorithms (EAs) and other metaheuristics are greatly affected by the choice of their parameters, not only as regards the precision of the solutions found, but also for repeatability, robustness, speed of convergence, and other properties. Most of these performance criteria are often conflicting with one another. In our work, we see the problem of EAs' parameter selection and tuning as a multi-objective optimization problem, in which the criteria to be optimized are precision and speed of convergence. We propose EMOPaT (Evolutionary Multi-Objective Parameter Tuning), a method that uses a well-known multi-objective optimization algorithm (NSGA-II) to find a front of non-dominated parameter sets which produce good results according to these two metrics.