One jump ahead: challenging human supremacy in checkers
One jump ahead: challenging human supremacy in checkers
Emergence: from chaos to order
Emergence: from chaos to order
Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
Principles in the Evolutionary Design of Digital Circuits—Part II
Genetic Programming and Evolvable Machines
Proceedings of the European Conference on Genetic Programming
Neutrality and the Evolvability of Boolean Function Landscape
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Coevolution of intelligent agents using cartesian genetic programming
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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A developmental model of neural network is presented and evaluated in the game of Checkers. The network is developed using cartesian genetic programs (CGP) as genotypes. Two agents are provided with this network and allowed to co-evolve untill they start playing better. The network that occurs by running theses genetic programs has a highly dynamic morphology in which neurons grow, and die, and neurite branches together with synaptic connections form and change in response to situations encountered on the checkers board. The method has no board evaluation function, no explicit learning rules and no human expertise at playing checkers is used. The results show that, after a number of generations, by playing each other the agents begin to play much better and can easily beat agents that occur in earlier generations. Such learning abilities are encoded at a genetic level rather than at the phenotype level of neural connections.