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.
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Batista, R., Segredo, E., Segura, C., León, C., Rodríguez, C. (2013). Solving the Unknown Complexity Formula Problem with Genetic Programming. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_22
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DOI: https://doi.org/10.1007/978-3-642-38679-4_22
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