Approximating the sheep milk production curve through the use of artificial neural networks and genetic algorithms

Authors:
Mercedes Torres;Cesar Hervás;Francisco Amador
Affiliations:
Facultad de Ciencias Económicas y Empresariales, ETEA, Universidad de Córdoba, C/Escritor Castilla Aguayo, no. 4 14004, Córdoba, Spain;Facultad de Ciencias Económicas y Empresariales, ETEA, Universidad de Córdoba, C/Escritor Castilla Aguayo, no. 4 14004, Córdoba, Spain;Facultad de Ciencias Económicas y Empresariales, ETEA, Universidad de Córdoba, C/Escritor Castilla Aguayo, no. 4 14004, Córdoba, Spain
Venue:
Computers and Operations Research
Year:
2005

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Abstract

This paper examines the potential of a neural network coupled with genetic algorithms to recognize the parameters that define the production curve of sheep milk, in which production is time-dependent, using solely the data registered in the animals' first controls. This enables the productive capacity of the animal to be identified more rapidly and leads to a faster selection process in determining the best producers. For this purpose we employ a network with a single hidden layer, using the property of "universal approximation". To find the number of nodes to be included in this layer, genetic and pruning algorithms are applied. Results thus obtained applying genetic and pruning algorithms are found to be better than other models which exclusively apply the classical learning algorithm Extended-Delta-Bar-Delta.