Adapt the Parameters of RBF Networks Using Grammatical Evolution

Ioannis G. Tsoulos; Alexandros Tzallas; Evangelos Karvounis

doi:10.20944/preprints202309.1532.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 21 September 2023 / Approved: 21 September 2023 / Online: 22 September 2023 (08:37:54 CEST)

RBF networks are used in a variety of real-world applications such as medical data or signal processing problems. The success of these parametric models lies in the successful adaptation of their parameters using efficient computational techniques. In the current work, a method of adjusting the parameters of these networks using Grammatical Evolution is presented. Grammatical Evolution will be used to successfully discover the most promising range of parameter values and then the training of the parameter set will be achieved using a Genetic Algorithm. The new method was applied to a wide range of data fitting and classification problems, and the results were more than promising.

Neural networks; Genetic algorithms; Genetic programming; Grammatical evolution

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Adapt the Parameters of RBF Networks Using Grammatical Evolution

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