An agent population can be evolved in a complex environment to perform various tasks and optimize its job performance using Learning Classifier System (LCS) technology. Due to the...
A new family of "Distribution Replacement” operators for use in steady state genetic algorithms is presented. Distribution replacement enforces the members of the populatio...
This paper takes an economic approach to derive an evolutionary learning model based entirely on the endogenous employment of genetic operators in the service of self-interested a...
We present a new method of Logic-Based Genetic Programming (LBGP). Using the intrinsic mechanism of backtracking in Prolog, we utilize large individual programs with redundant clau...
An evolutionary reinforcement-learning algorithm, the operation of which was not associated with an optimality condition, was instantiated in an artificial organism. The algorithm ...