Abstract
Co-evolution is a powerful problem-solving approach. However, fitness
evaluation in co-evolutionary algorithms can be computationally expensive, as
the quality of an individual in one population is defined by its interactions
with many (or all) members of one or more other populations. To accelerate
co-evolutionary systems, we introduce phylogeny-informed interaction
estimation, which uses runtime phylogenetic analysis to estimate interaction
outcomes between individuals based on how their relatives performed against
each other. We test our interaction estimation method with three distinct
co-evolutionary systems: two systems focused on measuring problem-solving
success and one focused on measuring evolutionary open-endedness. We find that
phylogeny-informed estimation can substantially reduce the computation required
to solve problems, particularly at the beginning of long-term evolutionary
runs. Additionally, we find that our estimation method initially jump-starts
the evolution of neural complexity in our open-ended domain, but
estimation-free systems eventually "catch-up" if given enough time. More
broadly, continued refinements to these phylogeny-informed interaction
estimation methods offers a promising path to reducing the computational cost
of running co-evolutionary systems while maintaining their open-endedness.