Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Artificial Life
A Lamarckian Approach for Neural Network Training
Neural Processing Letters
When Both Individuals and Populations Search: Adding Simple Learning to the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Advances in evolutionary computing
Development and the Baldwin effect
Artificial Life
Is there another new factor in evolution?
Evolutionary Computation
On the adaptive disadvantage of Lamarckianism in rapidly changing environments
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
A study of the Lamarckian evolution of recurrent neural networks
IEEE Transactions on Evolutionary Computation
Adding learning to cellular genetic algorithms for training recurrent neural networks
IEEE Transactions on Neural Networks
Balancing Population- and Individual-Level Adaptation in Changing Environments
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Co-evolution of Rewards and Meta-parameters in Embodied Evolution
Creating Brain-Like Intelligence
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 can be observed if phenotypic learning influences the evolutionary fitness of individuals, which can in turn accelerate or decelerate evolutionary change. Evidence for both learning-induced acceleration and deceleration can be found in the literature. Although the results for both outcomes were supported by specific mathematical or simulation models, no general predictions have been achieved so far. Here we propose a general framework to predict whether evolution benefits from learning or not. It is formulated in terms of the gain function, which quantifies the proportional change of fitness due to learning depending on the genotype value. With an inductive proof we show that a positive gain-function derivative implies that learning accelerates evolution, and a negative one implies deceleration under the condition that the population is distributed on a monotonic part of the fitness landscape. We show that the gain-function framework explains the results of several specific simulation models. We also use the gain-function framework to shed some light on the results of a recent biological experiment with fruit flies.