Optimization of classification tasks by using genetic algorithms

We present an attempt to separate between two kinds of events, using Genetic Algorithms. Events were produced by a Monte Carlo generator and characterized by the most discriminant variables. For the separation between events, two approaches are investigated. First, discriminant function parameters and neural network connection weights are optimized. In a multidimensional search approach, hyper-planes and hyper-surfaces are computed. In both cases, the performances are improved and the results compare favourably with other multivariate analysis.