Miconi, Thomas. “A collective genetic algorithm.” In Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation , pp. 876-883. 2001.
We take a look at the problem of collective evolution, and set the following goal : designing an algorithm that could allow a given population of agents to evolve incrementally, while they are performing their (possibly collaborative) task, with nothing more than a global fitness function to guide this evolution. We propose a simple algorithm that does just that, and apply it to a simple test problem (aggregation among animats controlled by feed-forward neural networks). We then show that under this form, this algorithm can only generate homogeneous systems. Seeing this as an unacceptable limitation, we modify our system in order to allow it to generate heterogeneous populations, in which semi-homogeneous sub-populations (i.e. sub-species) emerge and grow (or regress) naturally until a stable state is reached. We successfully apply this modified algorithm to a very simple toy-problem of simulated chemistry.