Using Neural Networks in Agent Teams to Speed Up Solution Discovery for Hard Multi-Criteria Problems

摘要

Hard multi-criteria (MC) problems are computationally intractable problems requiring optimization of more than one criterion. However, the optimization of two or more criteria tends to yield not just one optimal solution, but rather a set of non-dominated solutions. As a result, the evolution of a Pareto-Optimal set of non-dominated solutions from some population of can-didate solutions is often the most appropriate course of action. The non-dominated set of a population of solutions is comprised of those solutions whose criteria cannot all be dominated by those of at least one other solution in the current population.