A New Constructive Compound Neural Networks Using Fuzzy Logic and Genetic Algorithm 1 Application to Artificial Life

Jianjun YAN  Naoyuki TOKUDA  Juichi MIYAMICHI  

IEICE TRANSACTIONS on Information and Systems   Vol.E81-D       pp.1507-1516
Publication Date: 1998/12/25
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: Category: Bio-Cybernetics and Neurocomputing
neural networks,  artificial life,  fuzzy logic,  genetic algorithm,  network construction,  

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This paper presents a new compound constructive algorithm of neural networks whereby the fuzzy logic technique is explored as an efficient learning algorithm to implement an optimal network construction from an initial simple 3-layer network while the genetic algorithm is used to help design an improved network by evolutions. Numerical simulations on artificial life demonstrate that compared with the existing network design algorithms such as the constructive algorithms, the pruning algorithms and the fixed, static architecture algorithm, the present algorithm, called FuzGa, is efficient in both time complexity and network performance. The improved time complexity comes from the sufficiently small 3 layer design of neural networks and the genetic algorithm adopted partly because the relatively small number of layers facilitates an utilization of an efficient steepest descent method in narrowing down the solution space of fuzzy logic and partly because trappings into local minima can be avoided by genetic algorithm, contributing to considerable saving in time in the processing of network learning and connection. Compared with 54. 8 minutes of MLPs with 65 hidden neurons, 63. 1 minutes of FlexNet or 96. 0 minutes of Pruning, our simulation results on artificial life show that the CPU time of the present method reaching the target fitness value of 100 food elements eaten for the present FuzGa has improved to 42. 3 minutes by SUN's SPARCstation-10 of SuperSPARC 40 MHz machine for example. The role of hidden neurons is elucidated in improving the performance level of the neural networks of the various schemes developed for artificial life applications. The effect of population size on the performance level of the present FuzGa is also elucidated.