Genetic cascade learning for neural networks

Abstract

Genetic cascade learning is a new constructive algorithm for connectionist learning which combines genetic algorithms and the architectural feature of the cascade-correlation learning algorithm. Like the cascade-correlation learning architecture, this new algorithm also starts with a minimal network and dynamically builds a suitable cascade structure by training and installing one hidden unit at a time until the problem is successfully learned. This step-wise constructive algorithm exhibits more scalability than existing genetic algorithms and is free of the competing conventions problem which results from the fact that functionally equivalent networks may have different assignments of functionality to individual hidden units. Initial tests of genetic cascade learning are carried out on a difficult supervised learning problem as well as a reinforcement learning control problem. >