Tsoulos, I.G.; Tzallas, A.; Karvounis, E. Constructing the Bounds for Neural Network Training Using Grammatical Evolution. Computers2023, 12, 226.
Tsoulos, I.G.; Tzallas, A.; Karvounis, E. Constructing the Bounds for Neural Network Training Using Grammatical Evolution. Computers 2023, 12, 226.
Tsoulos, I.G.; Tzallas, A.; Karvounis, E. Constructing the Bounds for Neural Network Training Using Grammatical Evolution. Computers2023, 12, 226.
Tsoulos, I.G.; Tzallas, A.; Karvounis, E. Constructing the Bounds for Neural Network Training Using Grammatical Evolution. Computers 2023, 12, 226.
Abstract
Artificial neural networks are widely established models of computational intelligence that have been tested for effectiveness in a variety of real-world applications. These models require fitting a set of parameters through the use of some optimization technique. However, an issue that researchers often face is finding an efficient range of values for the parameters of the artificial neural network. This paper proposes an innovative technique of generating a promising range of values for the parameters of the artificial neural network. Finding the value field is done by a series of rules for partitioning the original set of values or expanding it, which rules are generated using Grammatical Evolution. After finding a promising interval of values, any optimization technique such as a genetic algorithm can be used to train the artificial neural network on that interval of values. The new technique was tested on a wide range of problems from the relevant literature and the results were extremely promising.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
