  The possibility to use competitive evolutionary algorithms to generate
long-term progress is normally prevented by the convergence on limit cycle
dynamics in which the evolving agents keep progressing against their current
competitors by periodically rediscovering solutions adopted previously over and
over again. This leads to local but not to global progress, i.e. progress
against all possible competitors. We propose a new competitive algorithm that
produces long-term global progress by identifying and by filtering out
opportunistic variations, i.e. variations leading to progress against current
competitors and retrogression against other competitors. The efficacy of the
method is validated on the co-evolution of predator and prey robots, a classic
problem that has been used in other related researches. The accumulation of
global progress over many generations leads to effective solutions that involve
the production of rather articulated behaviors. The complexity of the behavior
displayed by the evolving robots increases across generations although
progresses in performance are not always accompanied by behavior
complexification.
