Solving the unknown complexity formula problem with genetic programming

Authors:
Rayco Batista;Eduardo Segredo;Carlos Segura;Coromoto León;Casiano Rodríguez
Affiliations:
Dpto. Estadística, I. O. y Computación, Universidad de La Laguna, La Laguna, Santa Cruz de Tenerife, Spain;Dpto. Estadística, I. O. y Computación, Universidad de La Laguna, La Laguna, Santa Cruz de Tenerife, Spain;Dpto. Estadística, I. O. y Computación, Universidad de La Laguna, La Laguna, Santa Cruz de Tenerife, Spain;Dpto. Estadística, I. O. y Computación, Universidad de La Laguna, La Laguna, Santa Cruz de Tenerife, Spain;Dpto. Estadística, I. O. y Computación, Universidad de La Laguna, La Laguna, Santa Cruz de Tenerife, Spain
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Year:
2013

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Abstract

The Unknown Complexity Formula Problem (ucfp) is a particular case of the symbolic regression problem in which an analytical complexity formula that fits with data obtained by multiple executions of certain algorithm must be given. In this work, a set of modifications has been added to the standard Genetic Programming (gp) algorithm to deal with the ucfp. This algorithm has been applied to a set of well-known benchmark functions of the symbolic regression problem. Moreover, a real case of the ucfp has been tackled. Experimental evaluation has demonstrated the good behaviour of the proposed approach in obtaining high quality solutions, even for a real instance of the ucfp. Finally, it is worth pointing out that the best published results for the majority of benchmark functions have been improved.