Advantages Of Using Memetic Algorithms In The N-Person Iterated Prisoner’s Dilemma Game

Abstract

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.