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. By doing so, we can provide three kinds of results: (i) a method that is able to adapt parameters to a single function, (ii) a comparison between Differential Evolution (DE) and Particle Swarm Optimization (PSO) that takes into consideration both precision and speed, and (iii) an insight into how parameters of DE and PSO affect the performance of these EAs on different benchmark functions.

Analysis of Evolutionary Algorithms Using Multi-objective Parameter Tuning / Ugolotti, Roberto; Cagnoni, Stefano. - ELETTRONICO. - (2014), pp. 1343-1350. ((Intervento presentato al convegno GECCO 2014 - 2014 Conference on Genetic and Evolutionary Computation tenutosi a Vancouver, BC, Canada nel 2014 [10.1145/2576768.2598226].

Analysis of Evolutionary Algorithms Using Multi-objective Parameter Tuning

UGOLOTTI, Roberto;CAGNONI, Stefano
2014-01-01

Abstract

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. By doing so, we can provide three kinds of results: (i) a method that is able to adapt parameters to a single function, (ii) a comparison between Differential Evolution (DE) and Particle Swarm Optimization (PSO) that takes into consideration both precision and speed, and (iii) an insight into how parameters of DE and PSO affect the performance of these EAs on different benchmark functions.
9781450326629
Analysis of Evolutionary Algorithms Using Multi-objective Parameter Tuning / Ugolotti, Roberto; Cagnoni, Stefano. - ELETTRONICO. - (2014), pp. 1343-1350. ((Intervento presentato al convegno GECCO 2014 - 2014 Conference on Genetic and Evolutionary Computation tenutosi a Vancouver, BC, Canada nel 2014 [10.1145/2576768.2598226].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2748117
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