867 H A Hybrid System for Automatic Infant Cry Recognition II Carlos Alberto Reyes-García Instituto Nacional de Astrofísica Óptica y Electrónica, Mexico Sandra E. Barajas Instituto Nacional de Astrofísica Óptica y Electrónica, Mexico Esteban Tlelo-Cuautle Instituto Nacional de Astrofísica Óptica y Electrónica, Mexico Orion Fausto Reyes-Galaviz Universidad Autónoma de Tlaxcala, Mexico Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. INTRODUCTION Automatic Infant Cry Recognition (AICR) process is basically a problem of pattern processing, very similar to the Automatic Speech Recognition (ASR) process (Huang, Acero, Hon, 2001). In AICR frst we perform acoustical analysis, where the crying signal is analyzed to extract the more important acoustical features, like; LPC, MFCC, etc. (Cano, Escobedo and Coello, 1999). The obtained characteristics are represented by feature vectors, and each vector represents a pattern. These patterns are then classifed in their corresponding pathology (Ekkel, 2002). In the reported case we are automatically classifying cries from normal, deaf and asphyxiating infants. We use a genetic algorithm to fnd several optimal parameters needed by the Fuzzy Relational Neural Network FRNN (Reyes, 1994), like; the number of linguistic properties, the type of membership function, the method to calculate the output and the learning rate. The whole model has been tested on several data sets for infant cry classifcation. The process, as well as some results, is described. BACKGROUND In the frst part of this document a complete description of the AICR system as well as of the FRNN is given. So, with continuity purposes, in this part we will con- centrate in the description of the genetic algorithm and the whole system implementation and testing. A genetic algorithm refers to a model introduced and investigated by John Holland (John Holland, 1975) and by students of Holland (DeJong, 1975). Genetic algorithms are often viewed as function optimizers, although the range of problems to which genetic al- gorithms have been applied is quite broad. Recently, numerous papers and applications combining fuzzy concepts and genetic algorithms (GAs) have become known, and there is an increasing concern in the integra- tion of these two topics. In particular, there are a great number of publications exploring the use of GAs for developing or improving fuzzy systems, called genetic fuzzy systems (GFSs) (Cordon, Oscar, et al, 2001) (Casillas, Cordon, del Jesus, Herrera, 2000). EVOLUTIONARy DESIGN Within the evolutionary techniques, perhaps one of the most popular is the genetic algorithm (AG) (Goldberg, 1989). Its structure presents analogies with the biologi- cal theory of evolution, and is based on the principle of the survival of the fttest individual (Holland, 1975). Generally, a genetic algorithm has fve basic components (Michalewicz, 1992). A representation of potential solutions to the problem, a form to create potential initial solutions, a ftness function that is in charge to evaluate solutions, genetic operators that alter the offspring’s composition, and values for parameters like the size of the population, crossover probability, mutation probability, number of generations and oth- ers. Here we present different features of the genetic