Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Self-Organizing Maps
Development and the Baldwin effect
Artificial Life
Adding learning to the cellular development of neural networks: Evolution and the baldwin effect
Evolutionary Computation
Evolution, learning, and instinct: 100 years of the baldwin effect
Evolutionary Computation
Landscapes, learning costs, and genetic assimilation
Evolutionary Computation
Using learning to facilitate the evolution of features for recognizing visual concepts
Evolutionary Computation
Evolution, development and learning using self-modifying cartesian genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Imitation tendencies of local search schemes in baldwinian evolution
Proceedings of the 13th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
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 evolution-learning 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 basis for an abstract model displaying the Baldwin Effect in artificial neural networks that evolve, develop and learn.