HYBRID EVOLUTIONARY ALGORITHM FOR AUTOMATIC GENERATION NEURAL NETWORK CLASSIFICATORS
Hybrid evolutionary algorithm, that uses self-configuring genetic algorithm for neural network structure design and particle swarm optimization for neural network weights tuning, is developed, realized and investigated. The efficiency of application of the self-configuring method to stochastic algorithms for solving classification problems is demonstrated.