Evolution of Cooperation and Developmental Constraints: a GA-driven Approach

In this paper we present a model of evolution of cooperation driven by a Genetic Algorithm (GA) and a two-level fitness function representing cooperative and individualistic behaviours. The GA drives the evolution of artificial Genetic Regulatory Networks (GRNs) that controls colonies of artificial cells in a grid. This set-up is used to study the effect of computational complexity on the evolution of cooperation. Computational complexity being linked to the concept of developmental constraints in evolution. We show that there is a trade of between the computational complexity of a behaviour and the increase in fitness it bestows. Cooperation (being a more complicated behaviour than the individualistic one) will only (stably) evolve if the fitness reward of it is above a certain threshold. We also argue the importance of Artificia