Genetic Algorithms and Neural Networks

Abstract

Genetic algorithms are a class of optimization programs that can handle complicated problems. in essence they consist of a random-search method, made efficient by using explorative search. The method resembles evolutionary development in nature, using “mutation” and “mating”. in contrast to expert systems no heuristic knowledge is applied other than the ultimate goal. Artificial neural networks resemble the organization of the brain. Knowledge is stored in a multiple of “neurons” or nodes in a network. Input and output can be coded in any desired way. Neural networks are able to recognize patterns in a “fuzzy” way. The theory behind their internal behavior is not yet mature, but promising results have been obtained in analytical chemistry. © 1993, Elsevier Science & Technology All rights reserved.