Cooperative-Competitive Algorithms for Evolutionary Networks Classifying Noisy Digital Images

Authors:
A. D. Brown;H. C. Card
Affiliations:
Dept. of Electrical & Computer Engineering, The University of Manitoba, R3T 5V6 Winnipeg Manitoba, Canada;Dept. of Electrical & Computer Engineering, The University of Manitoba, R3T 5V6 Winnipeg Manitoba, Canada, e-mail: hcard@ee.umanitoba.ca
Venue:
Neural Processing Letters
Year:
1999

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Abstract

We describe an efficient method of combining theglobal search of genetic algorithms (GAs) with thelocal search of gradient descent algorithms. Eachtechnique optimizes a mutually exclusive subset of thenetwork‘s weight parameters. The GA chromosome fixesthe feature detectors and their location, and agradient descent algorithm starting from randominitial values optimizes the remaining weights. Threealgorithms having different methods of encoding hiddenunit weights in the chromosome are applied tomultilayer perceptrons (MLPs) which classify noisydigital images. The fitness function measures the MLPclassification accuracy together with the confidenceof the networks.