— In this paper, we lay the groundwork for extending our previously developed ASyMTRe architecture to enable constructivist learning for multi-robot team tasks. The ASyMTRe archi...
The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). ...
A. E. Eiben, Mark Horvath, Wojtek Kowalczyk, Marti...
Abstract. In this article we present EANT2, a method that creates neural networks (NNs) by evolutionary reinforcement learning. The structure of NNs is developed using mutation ope...
An evolutionary reinforcement-learning algorithm, the operation of which was not associated with an optimality condition, was instantiated in an artificial organism. The algorithm ...
This paper reports briefly on the development of a new approach to evolutionary computation, called the Learnable Evolution Model or LEM. In contrast to conventional Darwinian-typ...
Ryszard S. Michalski, Guido Cervone, Kenneth A. Ka...