Methods for decreasing the number of objective evaluations for independent computationally expensive objective problems

— In this paper, three new methods for pushing solutions toward a desired region of the objective space more quickly are explored; hypercube distance scaling, dynamic objective thresholding, and hypercube distance objective ordering. These methods are applicable for problems that do not require the entire Pareto front and that require an independent computationally expensive computation for each objective. The performance of these methods is evaluated with the multiple objective 0/1 Knapsack problem.

• Type: Conference
• Year: 2008
• Where: CEC
• Authors: Greg Rohling
• Keywords: Artificial Intelligence, CEC 2008, Distance Objective Ordering, Dynamic Objective Thresholding, Hypercube Distance