Evolutionary dynamics of structured genetic algorithms

People: Xin Wang, Felix Hol, Samuel Scarpino and Florian Sabou

The spatial structure of an evolving (meta)population can have profound effects on its evolutionary dynamics. We investigate and quantify the ability of populations with different population structures to evolve.

The computational model we use for our study extends on work by Mitchell & Crutchfield (and coworkers) in which a genetic algorithm (GA) evolves a population of cellular automata (CA) rules to perform a density classification task.

Modeling evolutionary dynamics of spatially structured populations -- I am very interested in the interplay between ecology and evolution. Motivated by Sewall Wright's seminal 1932 paper I would like to investigate the effect that metapopulation formation has on the speed of evolution. The computational model to investigate this could be based on work by Mitchell & Crutchfield (and coworkers) where a genetic algorithm evolves a population of cellular automata to perform a certain task (see this paper). I propose to embed population structure in the genetic algorithm (GA) and find out what effect this has on the capabilities of the GA. One way of doing this might be to (bluntly) define subpopulations that cross breed at specified intervals; but I am sure that there are much more elegant/sophisticated ways of embedding structure. Some (wild) ideas for this are: putting them on graphs or lattices (with or without vacancies/movement)…. Any suggestions are greatly appreciated! (also other ideas for modeling stuff like this (evolutionary game theory etc..) are welcome)