Younger is better
Alexandre Coninx, Stéphane Doncieux · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2021
What makes a quality-diversity (QD) algorithm effective? This question is increasingly studied as such algorithms gain in popularity. Recent work on Novelty Search linked its efficiency to the evolution of a population under a dynamic selection pressure defined by the novelty metric, which allows the population to converge to highly evolvable individuals, on which spending the evolutionary budget allows to navigate the behavior space. MAP-Elites, another popular QD algorithm, does not use an explicit population, instead sampling the parent population directly from the behavioral archive at each generation. This sampling can be uniform, or biased according to criteria such as curiosity, which favor individuals that previously generated successful offsprings. In this article, we show that such improved selection schemes are efficient because they create a dynamic pseudo-population that mimics the desirable qualities of a Novelty Search population. We do this by proposing another simpler selection scheme, youth, that simply focuses the evolutionary budget on such a dynamic pseudo-population, and by showing it exhibits similar behavior and performance as curiosity without explicitly selecting for previously successful parents. This has important implications for the design of future QD algorithms taking the best of both archive-based and population-based methods.