Artificial Life Optimization over Complex Networks

Different topologies for the life space of an artificial life environment facing continuous optimization problems are explored. Starting from a lattice we tested the efficiency of the algorithm when the physical space in which the agents evolve is represented first by a higher degree lattice, then by a small world network, next by a random graph and last by a scale-free network. Interesting results are obtained about efficiency improvement with respect to the lattice in all cases, but the most relevant are those ones connected to the scale-free topology. Even if obtained on a particular implementation of an evolutionary algorithm, results shown are supposed to hold for a larger class of artificial environment and/or evolutionary contexts implemented in literature. First empirical explanations of the observed phenomena are given.