Genetic algorithms are a robust adaptive optimization method based on biological principles. A population of strings representing possible problem solutions is maintained. Search proceeds by recombining strings in the population. The theoretical foundations of genetic algorithms are based on the notion that selective reproduction and recombination of binary strings changes the sampling rate of hyperplanes in the search space so as to reflect the average fitness of strings that reside in any particular hyperplane. Thus, genetic algorithms need not search along the contours of the function being optimized and tend not to become trapped in local minima. This paper is an overview of several different experiments applying genetic algorithms to neural network problems. These problems include 1. (1) optimizing the weighted connections in feed-forward neural networks using both binary and real-valued representations, and 2. (2) using a genetic algorithm to discover novel architectures in the form of connectivity patterns for neural networks that learn using error propagation. Future applications in neural network optimization in which genetic algorithm can perhaps play a significant role are also presented. © 1990.
