Coevolutionary intransitivity in games: A landscape analysis
This work addresses a theoretical issue in coevolutionary algorithms for researchers, but it appears incremental as it builds on existing concepts without major breakthroughs.
The paper tackled the problem of intransitivity in coevolutionary games, which hinders progress and superiority, by linking measures of intransitivity to fitness landscapes that address subjective vs. objective fitness, and demonstrated this through numerical experiments on a random game with adjustable randomness.
Intransitivity is supposed to be a main reason for deficits in coevolutionary progress and inheritable superiority. Besides, coevolutionary dynamics is characterized by interactions yielding subjective fitness, but aiming at solutions that are superior with respect to an objective measurement. Such an approximation of objective fitness may be, for instance, generalization performance. In the paper a link between rating-- and ranking--based measures of intransitivity and fitness landscapes that can address the dichotomy between subjective and objective fitness is explored. The approach is illustrated by numerical experiments involving a simple random game with continuously tunable degree of randomness.