Use of a mixed radix fitness function to evolve swarm behaviors

Architecting systems designed to elicit group-level behavior beyond the capability of any single agent, however, demands a labor and experimentation-intensive cycle on the part of the programmer. As part of a system to evolve swarm behaviors, we have developed a mixed radix fitness function to overcome the problems encountered with typical fitness functions when used in a multi-objective optimization problem. In this work, we show that mixed radix fitness functions can be used to encode sequential dependencies and prioritize metrics within the context of agent-based swarm behavior. To demonstrate the effectiveness of our approach, we construct a mixed radix fitness function and evolve swarm algorithms to solve a complex extension of the classic object collection problem. Further, we show the mixed radix fitness function is successful in driving evolution towards a feasible solution while avoiding local extrema.