abstract = "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 until 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.",