The Influence of Different Cost Functions in Global Optimization Techniques

  • Authors:
  • Cleber Zanchettin;Teresa B. Ludermir

  • Affiliations:
  • Federal University of Pernambuco, Brazil;Federal University of Pernambuco, Brazil

  • Venue:
  • SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
  • Year:
  • 2006

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Abstract

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.