Using neural networks in agent teams to speedup solution discovery for hard multi-criteria problems

Evolutionary population-based search methods are often used to find a Pareto-optimal set of solutions for hard multicriteria optimization problems. We utilize one such agent architecture to evolve good solution sets to these problems, deploying agents to progressively add, modify and delete candidate solutions in one or more populations over time. Here we describe how we assign neural nets to aid agent decision-making and encourage cooperation to improve convergence to good Pareto optimal solution sets. This paper describes the design and results of this approach and suggests paths for further study.