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GECCO
2010
Springer

The baldwin effect in developing neural networks

9 years 3 months ago
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
Keith L. Downing
Added 23 Aug 2010
Updated 23 Aug 2010
Type Conference
Year 2010
Where GECCO
Authors Keith L. Downing
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