Biological Cybernetics
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Generalization and scaling in reinforcement learning
Advances in neural information processing systems 2
Exploring adaptive agency II: simulating the evolution of associative learning
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Intelligent behaviour in animals and robots
Intelligent behaviour in animals and robots
Simulations combining evolution and learning
Adaptive individuals in evolving populations
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
When Both Individuals and Populations Search: Adding Simple Learning to the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
IEEE Expert: Intelligent Systems and Their Applications
Valency for adaptive homeostatic agents: relating evolution and learning
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
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For learning to improve the adaptiveness of an animal's behavior, and thus direct evolution in the way Baldwin suggested, the learning mechanism must incorporate an innate evaluation of how the animal's actions influence its reproductive fitness. For example, many circumstances that damage an animal or otherwise reduce its fitness are painful and tend to be avoided. We refer to the mechanism by which an animal evaluates the fitness consequences of its actions as a “motivation system,” and argue that such a system must evolve along with the behaviors it evaluates. We describe simulations of the evolution of populations of agents instantiating a number of different architectures for generating action and learning in worlds of differing complexity. We find that in some cases, members of the populations evolve motivation systems that are accurate enough to direct learning so as to increase the fitness of the actions that the agents perform. Furthermore, the motivation systems tend to incorporate systematic distortions in their representations of the worlds they inhabit; these distortions can increase the adaptiveness of the behavior generated.