Co-evolutionary learning on noisy tasks
Paul J. Darwen, Jordan B. Pollack · 2003
The paper studies the effect of noise on co-evolutionary learning, using Backgammon as a typical noisy task. It might seem that co-evolutionary learning would be ill-suited to noisy tasks: genetic drift causes convergence to a population of similar individuals, and on noisy tasks it would seem to require many samples (i.e., many evaluations and long computation time) to discern small differences between similar population members. Surprisingly, the paper learns otherwise: for small population sizes, the number of evaluations does have an effect on learning; but for sufficiently large populations, more evaluations do not improve learning at all-population size is the dominant variable. This is because a large population maintains more diversity, so that the larger differences in ability can be discerned with a modest number of evaluations. This counter-intuitive result means that co-evolutionary learning is a feasible method for noisy tasks, such as military situations and investment management.