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.
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