title: Cooperative coevolution of neural representations.
abstract: A genetic algorithm (GA) is used to search for a set of local feature detectors or hidden units. These are in turn employed as a representation of the input data for neural learning in the upper layer of a multilayer perceptron (MLP) which performs an image classification task. Three different methods of encoding hidden unit weights in the chromosome of the GA are presented, including one which coevolves all the feature detectors in a single chromosome, and two which promote the cooperation of feature detectors by encoding them in their own individual chromosomes. The fitness function measures the MLP classification accuracy together with the confidence of the networks.
authors.affiliation: Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada.
authors.forename: A D
authors.initials: AD
authors.lastname: Brown
authors.forename: H C
authors.initials: HC
authors.lastname: Card
mesh_terms: D000465:Algorithms; D008957:Models, Genetic; D008959:Models, Neurological; D016571:Neural Networks, Computer