title: A combined evolution method for associative memory networks.
abstract: In the study of associative memory networks, the updating rule remains unchanged during neuron evolution. When evolution stops at an undesired minimum, simulated annealing is used to resolve the problem. In this paper, a combined neuron evolution method based on a multipath network architecture is presented. It is shown that, by controlling the evolution path, the probability that evolution terminates in undesired, minima is significantly reduced. Once evolution is trapped in an undesired minimum with respect to one path, the method seeks an alternative path to carry on the evolution. The process continues until a desired minimum is reached. Visual examples are used to demonstrate the performance of the method.
authors.affiliation: Department of Electrical Engineering, University of Sydney, Sydney, Australia
authors.forename: Andrew C.C.
authors.initials: AC
authors.lastname: Cheng
authors.forename: Ling
authors.initials: L
authors.lastname: Guan