Varying sample sizes for the co-evolution of heterogeneous agents

The evolution of a heterogeneous team is a complex problem. Evolving teams if a single population can retard the GA´s ability to specialize emergent behavior, but co-evolution requires a system for evaluation at trial time. If two few combinations of partners are tested, the GA is unable to recognize fit agents; if too many agents are tested, the resultant computation time becomes excessive. We created a system based on punctuated anytime learning that only periodically tests samples of partner combinations to reduce computation time and tested a variety of sample sizes. In this paper, we present a successful method of varying the sample sizes, dependent on the level of fitness, using a box pushing task for comparison.