Coevolution of Neural Networks Using a Layered Pareto Archive
(2005) Evolutionary computation (EC) can be used to discover
good game playing strategies with respect to a fixed group of
existing opponents. Competitive coevolution (CC) is a
generalization of EC in which the opponents also evolve, making
it possible to find strategies for games in which no good
players already exist. Since the performance criterion in CC
changes over generations, a Coevolutionary Memory (CM) of good
opponents is required to avoid regress. The layered Pareto
Coevolution Archive (LAPCA) was recently proposed as an
effective CM that guarantees monotonic progress under certain
assumptions. The main limitation of LAPCA is that it requires
numerous game evaluations because it has to determine Pareto-
dominance relations between players. The Hall of Fame (HOF),
consisting of previous generation champions, is an easier CM to
implement and needs no extra evaluations besides those required
by evolution. While the LAPCA has only been demons