The Use of Genetic Algorithms and Neural Networks to Investigate the Baldwin Effect

Michael Jones
Trinity University
San Antonio, Texas
mjones@trinity.edu - Aaron Konstam
Trinity University
San Antonio, Texas
akonstam@trinity.edu

KEYWORDS: genetic algorithms, neural networks, learning, Baldwin effect

Abstract:

Standard evolutionary theory states that learned information will not be transferred into an underlying genotype. There is, however, a hypothesis that is consistent with the belief that learned behavior somehow influences the course of evolution. This hypothesis is called the Baldwin effect and it has been shown to occur in experiments with artificial life by Hinton and Nowlan and Ackley and Littman. A analysis was done of the effects of mutation and crossover rates on a computational model of the Baldwin effect which showed that this effect is most pronounced in asexual populations with low mutation rates. It was also noticed that the learning that occurred through the Baldwin effect exhibited the punctuated equilibrium behavior that is believed to be a part of all evolution.