Islands of cooperation emerge by stigmergic interactions in iterated spatial games.

This article focuses on the emergence of cooperation in societies of self-interested agents. In particular, it introduces a mechanism based on indirect-stigmergic-interactions between agents moving in an environment, to express the likeliness of finding cooperative partners. On the one hand, agents that find themselves cooperating with others emit pheromones in their current location, expressing the presence of agents willing to cooperate. On the other hand, agents that sense pheromones tend to move towards regions with a higher pheromone concentration. Results show that this mechanism leads to the emergence of spatial regions where cooperation can be effectively sustained, and in which agents can overall get better payoffs than those agents not taking into account pheromones in their choices.

Evolving binary classifiers through parallel computation of multiple fitness cases.

This paper describes two versions of a novel approach to developing binary classifiers, based on two evolutionary computation paradigms: cellular programming and genetic programming. Such an approach achieves high computation efficiency both during evolution and at runtime. Evolution speed is optimized by allowing multiple solutions to be computed in parallel. Runtime performance is optimized explicitly using parallel computation in the case of cellular programming or implicitly taking advantage of the intrinsic parallelism of bitwise operators on standard sequential architectures in the case of genetic programming. The approach was tested on a digit recognition problem and compared with a reference classifier.