2005 Special Issue: A model of evolution and learning

Authors:
Vladimir G. Red'ko;Oleg P. Mosalov;Danil V. Prokhorov
Affiliations:
Institute of Optical Neural Technologies, Russian Academy of Science, Vavilova Str., 44/2, Moscow 119333, Russian Federation;Moscow Institute of Physics and Technologies, Institutsky per., 9, Dolgoprudny, Moscow region 141700, Russian Federation;Research and Advanced Engineering, Ford Motor Company, 2101 Village Road, MD 2036, Dearborn, MI 48124, USA
Venue:
Neural Networks - 2005 Special issue: IJCNN 2005
Year:
2005

Citing 3
Cited 1

Adaptive individuals in evolving populations: models and algorithms

Adaptive individuals in evolving populations: models and algorithms
Selection behaviour in caddis fly larvae

Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Adaptive critic designs

IEEE Transactions on Neural Networks

Project "Animat Brain": Designing the Animat Control System on the Basis of the Functional Systems Theory

Anticipatory Behavior in Adaptive Learning Systems

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Abstract

We study a model of evolving populations of self-learning agents and analyze the interaction between learning and evolution. We consider an agent-broker that predicts stock price changes and uses its predictions for selecting actions. Each agent is equipped with a neural network adaptive critic design for behavioral adaptation. We discuss three cases in which either evolution or learning, or both, are active in our model. We show that the Baldwin effect can be observed in our model, viz. originally acquired adaptive policy of best agent-brokers becomes inherited over the course of the evolution. We also compare the behavioral tactics of our agents to the searching behavior of simple animals.