A cellular genetic programming approach to classification
Gianluigi Folino, Clara Pizzuti, Giandomenico Spezzano · 1999
A cellular genetic programming approach to data classification is proposed. The method uses cellular automata as a framework to enable a fine-grained parallel implementation of GP through the diffusion model. The main advantages to employ the method for classification problems consist in handling large populations in reasonable times, enabling fast convergence by reducing the number of iterations and execution time, favouring the cooperation in the search for good solutions, thus improving the accuracy of the method. 1 Introduction Data classification is a learning process that identifies common characteristics in a set of objects contained in a database and categorises them into different groups (classes). To build a classification a sample of the tuples (also called examples) of the database is considered as the training set. Each tuple is composed of the same set of attributes, or features, which are used to distinguish them, and an additional known class attribute tha...