B. Beliczynski et al. (Eds.): ICANNGA 2007, Part I, LNCS 4431, pp. 276–285, 2007. © Springer-Verlag Berlin Heidelberg 2007 Automatic Design of ANNs by Means of GP for Data Mining Tasks: Iris Flower Classification Problem Daniel Rivero, Juan Rabuñal, Julián Dorado, and Alejandro Pazos Department of Information & Communications Technologies, Campus Elviña 15071, A Coruña, Spain {drivero,julian,juanra,apazos}@udc.es http://sabia.tic.udc.es Abstract. This paper describes a new technique for automatically developing Artificial Neural Networks (ANNs) by means of an Evolutionary Computation (EC) tool, called Genetic Programming (GP). This paper also describes a prac- tical application in the field of Data Mining. This application is the Iris flower classification problem. This problem has already been extensively studied with other techniques, and therefore this allows the comparison with other tools. Re- sults show how this technique improves the results obtained with other tech- niques. Moreover, the obtained networks are simpler than the existing ones, with a lower number of hidden neurons and connections, and the additional ad- vantage that there has been a discrimination of the input variables. As it is ex- plained in the text, this variable discrimination gives new knowledge to the problem, since now it is possible to know which variables are important to achieve good results. 1 Introduction ANNs are learning systems that have solved a large amount of complex problems related to different disciplines (classification, clustering, regression, etc.) [1]. The interesting characteristics of this powerful technique have induced its use by research- ers in different environments [2]. Nevertheless, the use of ANNs has some problems mainly related to their devel- opment process. This process can be divided into two parts: architecture development and training and validation. As the network architecture is problem-dependant, the design process of this architecture used to be manually performed, meaning that the expert had to test different architectures and train them until finding the one that achieved best results after the training process. The manual nature of the described process determines its slow performance although the recent use of ANNs creation techniques have contributed to achieve a more automatic procedure. The technique described in this paper allows the automatically obtaining of ANNs with no need of human participation. This technique is applied to a well-known prob- lem: the Iris Flower classification Problem [3]. Results show how this technique can solve the problem. Moreover, it is capable of obtaining simpler networks than the existing ones for solving this problem.