Analyzing meme propagation in multimemetic algorithms: Initial investigations

Multimemetic algorithms (MMAs) are a subclass of memetic algorithms in which memes are explicitly attached to genotypes and evolve alongside them. We analyze the propagation of memes in MMAs with spatial structure. For this purpose we propose an idealized selecto-Lamarckian model that only features selection and local improvement, and study under which conditions good, high-potential memes can proliferate. We compare population models with panmictic and toroidal grids topology. We show that the increased takeover time induced by the latter is essential to improve the chances for good memes to express themselves in the population by improving their hosts, hence enhancing their survival rates.