Coevolution of neural Go players in a cultural environment

We present experiments (co)evolving Go players based on artificial neural networks (ANNs) for a 5×5 board. ANN structure and weights are encoded in multi-chromosomal genotypes. In evolutionary scenarios, a population of generalized multi-layer perceptrons (GMLPs) has to compete with a single Go program from a set of three players of different quality. Two coevolutionary approaches, namely, a dynamically growing culture, and a fixed-size elite represent the changing environment of the coevolving population. The playing quality of the (co)evolved players is measured by a strength value derived from games against the set of three programs. We also report on first experiments employing recurrent networks, which allow a direct structural representation of the Go board. Finally, the quality of all the best (co)evolved players is evaluated in a round robin tournament.