The Influence of Different Cost Functions in Global Optimization Techniques

This work presents an evaluation of the effect of different cost functions in a methodology that integrates heuristic tabu search, simulated annealing, genetic algorithms and backpropagation. We investigated four cost function approaches: average method, weight-decay, multi-objective optimization, combined multi-objective and weight-decay. The weight-decay approach presented promising results in the simultaneous optimization of artificial neural network architecture and weights. The experiments were performed in four classifications and one prediction problem.