How many neurons?: a genetic programming answer

Authors:
Leonardo Trujillo;Yuliana Martínez;Patricia Melin
Affiliations:
Instituto Tecnológico de Tijuana, Tijuana, Mexico;Instituto Tecnológico de Tijuana, Tijuana, Mexico;Instituto Tecnológico de Tijuana, Tijuana, Mexico
Venue:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Year:
2011

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Abstract

The goal of this paper is to derive predictive models that take as input a description of a problem and produce as output an estimate of the optimal number of hidden nodes in an Artificial Neural Network (ANN). We call such computational tools Direct Estimators of Neural Network Topology (DENNT), an use Genetic Programming (GP) to evolve them. The evolved DENNTs take as input statistical and complexity descriptors of the problem data, and output an estimate of the optimal number of hidden neurons.