Evolving Heterogeneous Neural Agents by Local Selection
Filippo Menczer, W. Nick Street, Melania Degeratu · The MIT Press eBooks · 2001
Evolutionary algorithms have been applied to the synthesis of neural architectures, but they normally lead to uniform populations. Homogeneous solutions, however, are inadequate for certain applications and models. For these cases, local selection may produce the desired heterogeneity in the evolving neural networks. This chapter describes algorithms based on local selection, and discusses the main differences distinguishing them from standard evolutionary algorithms. The use of local selection to evolve neural networks is illustrated by surveying previous work in three domains (simulations of adaptive behavior, realistic ecological models, and browsing information agents), as well as reporting on new results in feature selection for classification.