Hybrid computational intelligence with evolutionary computation and object neural networks
David B. Fogel · Hybrid Intelligent Systems · 2003
Evolutionary computation provides a basis for computer self-learning in the absence of human expertise. Evolutionary algorithms can be combined usefully with diverse other structures and methods of computational intelligence and AI to optimize performance, adapt to changing circumstances, and meet goals in a range of environments. Experiments with have recently shown that an evolutionary algorithm can be combined with neural networks to learn to play checkers (draughts) at a level that is comparable with human experts, even without relying on human expertise in selecting features to evaluate alternative positions. The results from this Blondie24 project will be reviewed. New efforts that extend the approach to evolving object neural networks, which focus on specific areas of concern as defined by human expertise, in the game of chess will be detailed. The potential for allowing an evolutionary process to learn how to focus its attention without human guidance will be discussed. Members in the audience will have the opportunity to challenge Blondie24 to a game of checkers, and may also have the opportunity to compete against chess program that has evolved to the master level of competition.