Session:

Evolutionary Computation and Neural Networks (ECNN)
Monday March 01, 2004, 12.40 – 13.00

Session Chair:

Paulo Cortez, Miguel Rocha

Paper Title:

A Lamarckian Model Combining Levenberg-Maquardt Algorithm and a Genetic Algorithm

Author(s):

P. Pires, Universidade Portucalense – Departamento de Gestão Porto, Portugal
P. Castro, Escola Superior de Tecnologia e Gestão do Instituto Politécnico de Viana do Castelo , Portugal

Abstract:

We review the integration between the genetic and evolutionary techniques with artificial neural networks. A Lamarckian model is proposed based on genetic algorithms and artificial neural networks. The genetic algorithm evolves the population while the artificial neural network performs the learning process. The direct encoding scheme was used. This model was submitted to several data sets and provided good results, exhibiting superior robustness when compared with the Levenberg-Marquardt and the Scaled Conjugate Gradient algorithms. It also achieved the best solutions in the regression problems.