Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Cognitive Neuroscience
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The purpose of this article is to make a contribution to the study of modular structure of neural nets, in particular to describe a method of automatic neural net modularization. The problem specific modularizations of the representation emerge through the iterations of the evolutionary algorithm directly with the problem. We used the probability vector to construct n - bit vectors, which represented individuals in the population (in our approach they describe an architecture of a neural network). All individuals in every generation are pseudorandomly generated from the probability vector that is associated with this generation. The probability vector is updated on the basis of best individuals in a population, so that next generations are getting progressively closer to best solutions. The process is repeated until the probability vector entries are close to zero or to one. The resulting probability vector then determines an optimal solution of the given optimization task.