Loudspeaker Filter Design With AI

Genetic Algorithm Selection Methods

Bachelor thesis (2023)

Authors

K.L. Bavelaar Electrical Engineering, Mathematics and Computer Science
J.F.J. Verweij Electrical Engineering, Mathematics and Computer Science

Contributors

G.J.M. Janssen Signal Processing Systems - EEMCS (mentor)
S.D. Cotofana Computer Engineering - EEMCS (graduation committee member)
Filipe Arroyo Cardoso Electronic Instrumentation - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Koen Bavelaar, Jeroen Verweij
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Published Date

30-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This thesis details the design of a selection operator used in a Genetic Algorithm. The Genetic Algorithm is used for loudspeaker filter design of three way loudspeakers for which tournament selection was chosen as selection operator. A methodology is proposed and used to tune the parameters of tournament selection, which is based on diversity and fitness of the population. Besides basic tournament selection, two new adaptive selection operators based on tournament selection are proposed to improve its functionality. The first adaptive selection operator uses noise proportional to the fitness variance of the population to improve the efficiency of the genetic algorithm. The second adaptive selection operator uses a convergence stage to speed up the convergence towards the optimal filter. After the presented tuning process in this thesis, the latter adaptive selection operator was found to perform better. The optimal selection operator and parameters found in this thesis will not translate to every application, because they heavily depend on the design and the application of the genetic algorithm. However, the presented comparison of selection operators, the provided performance metrics and design methodology can still be used to guide the choice and the tuning process of a selection operator used in any genetic algorithm.