Cooperative-Competitive Algorithms for Evolutionary Networks Classifying Noisy Digital Images

We describe an efficient method of combining the global search of genetic algorithms (GAs) with the local search of gradient descent algorithms. Each technique optimizes a mutually exclusive subset of the network's weight parameters. The GA chromosome fixes the feature detectors and their location, and a gradient descent algorithm starting from random initial values optimizes the remaining weights. Three algorithms having different methods of encoding hidden unit weights in the chromosome are applied to multilayer perceptrons (MLPs) which classify noisy digital images. The fitness function measures the MLP classification accuracy together with the confidence of the networks.