The N-Person Iterated Prisoner's Dilemma (NIPD) is an interesting game that has proved to be very useful to explore the emergence of cooperation in multi-player scenarios. Within this game, the way that agents are interconnected is a key element that influences cooperation. In this context, complex networks provide a realistic model of the topological features found in Nature and in many social and technological networks. Considering these networks, it is interesting to study the network evolution, given the possibility that agents can change their neighbors (dynamic rewire), when non-cooperative behaviors are detected. In this paper, we present a model of the NIPD game where a population of genetically-coded agents compete altogether. We analyze how different game parameters, and the network topology, affect the emergence of cooperation in static complex networks. Based on that, we present the main contribution of the paper that concerns the influence of dynamic rewiring in the emergence of cooperation over the NIPD.

Memetic algorithms are a type of genetic algorithms very valuable in optimization problems. They are based on the concept of "meme", and use local search techniques, which allow them to avoid premature convergence to suboptimal solutions. Among these algorithms we can consider Lamarckian and Baldwinian models, depending on whether they modify (the former) or not (the latter) the agent's genotype. In this paper we analyze the application of memetic algorithms to the N-Person Iterated Prisoner's Dilemma (NIPD). NIPD is an interesting game that has proved to be very useful to explore the emergence of cooperation in multi-player scenarios. The main contributions of this paper are related to setting the ground to understand the implications of the memetic model and the related parameters. We investigate to which extent these decisions determine the level of cooperation obtained as well as the memory and the execution performance.

Abstract. Geocomputation has a long tradition in dealing with fuzzyness in different contexts, most notably in the challenges created by the representation of geographic space in digital form. Geocomputation tools should be able to address the imminent continuous nature of geo phenomena, and its accompanying fuzzyness. Fuzzy Set Theory allows partial memberships of entities to concepts with non-crisp boundaries. In general, the application of fuzzy methods is distance-based and for that reason is insensible to changes in density. In this paper a new method for defining density-based fuzzy membership functions is proposed. The method automatically determines fuzzy membership coefficients based on the distribution density of data. The density estimation is done using a Self-Organizing Map (SOM). The proposed method can be used to accurately describe clusters of data which are not well characterized using distance methods. We show the advantage of the proposed method over traditional distance-based membership functions.

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