The baldwin effect in developing neural networks

The Baldwin Effect is a very plausible, but unproven, biological theory concerning the power of learning to accelerate evolution. Simple computational models in the 1980’s gave the first constructive proof of its potential existence, and subsequent work in evolutionary computation has shown the practical, computational, advantages of hybrid evolutionlearning systems . However, the basic theory, particularly its second phase (involving genetic assimilation of acquired characteristics) is difficult to reconcile in systems controlled by neural networks, particularly those that arise from their genotypes via a complex developmental process. Our research uses new evidence of the blurred distinction between development and learning in natural neural systems as the r an abstract model displaying the Baldwin Effect in artificial neural networks that evolve, develop and learn. Categories and Subject Descriptors I.2.6[Learning]: Connectionism and Neural Nets General Terms Algorithms

• Type: Conference
• Year: 2010
• Where: GECCO
• Authors: Keith L. Downing
• Keywords: Baldwin Effect, GECCO 2010, Neural Networks, Optimization, first Constructive Proof