Unifying Learning with Evolution Through Baldwinian Evolution and Lamarckism: A Case Study

Baldwinian evolution and Lamarckism hold that behaviour is not evolved solely through crossover and mutation, but also through learning. In the former case, learned behaviour causes changes only to the fitness landscape, whilst in the latter, learned behaviour also causes changes to the parents' genotypes. Although the biological plausibility of these positions remains arguable, they provide a potentially useful framework for the construction of artificial systems. As an example, I show the use and effect of Baldwinian and Lamarckian evolution in the design of the hidden layer of a RBF network. Here, evolution is used to optimise the k-means clustering process by co-evolving the two determinant parameters of the network's layout (i.e., the number of centroids and the centroids' positions)