Differential Evolution Optimal Parameters Tuning with Artificial Neural Network

Manu Centeno-Telleria, Ekaitz Zulueta, Unai Fernandez-Gamiz, Daniel Teso-Fz-Betoño and Adrián Teso-Fz-Betoño
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Manu Centeno-Telleria: System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Ekaitz Zulueta: System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Unai Fernandez-Gamiz: Nuclear Engineering and Fluid Mechanics Department, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Daniel Teso-Fz-Betoño: System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Adrián Teso-Fz-Betoño: System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain

Mathematics, 2021, vol. 9, issue 4, 1-20

Abstract: Differential evolution (DE) is a simple and efficient population-based stochastic algorithm for solving global numerical optimization problems. DE largely depends on algorithm parameter values and search strategy. Knowledge on how to tune the best values of these parameters is scarce. This paper aims to present a consistent methodology for tuning optimal parameters. At the heart of the methodology is the use of an artificial neural network (ANN) that learns to draw links between the algorithm performance and parameter values. To do so, first, a data-set is generated and normalized, then the ANN approach is performed, and finally, the best parameter values are extracted. The proposed method is evaluated on a set of 24 test problems from the Black-Box Optimization Benchmarking (BBOB) benchmark. Experimental results show that three distinct cases may arise with the application of this method. For each case, specifications about the procedure to follow are given. Finally, a comparison with four tuning rules is performed in order to verify and validate the proposed method’s performance. This study provides a thorough insight into optimal parameter tuning, which may be of great use for users.

Keywords: evolutionary algorithm; differential evolution; parameter tuning; artificial neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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