A Fast Multi-Objective Evolutionary Algorithm for Expensive Simulation Optimization Problems

This paper describes a multi-objective evolutionary algorithm which targets primarily on "expensive" simulation-based optimization problems. The idea is to approximate the Pareto optimal front using response surface methodology and screen out less promising offspring solutions before they are evaluated via simulation runs. Numerical examples suggest that the algorithm can save computational efforts without degrading the quality of final solutions.