A pareto following variation operator for fast-converging multiobjective evolutionary algorithms

One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate (or surrogate) methods offer the possibility of reducing the number of evaluations, without reducing solution quality. Artificial Neural Network (ANN) based models are one approach that have been used to approximate the future front from the current available fronts with acceptable accuracy levels. However, the associated computational costs limit their effectiveness. In this work, we introduce a simple approach that has comparatively smaller computational cost and we have developed this model as a variation operator that can be used in any kind of multiobjective optimizer. When designing this model, we have considered the whole search procedure as a dynamic system that takes available objective values in current front as input and generates approximated design variables for the next front as output. Init...

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: A. K. M. Khaled Ahsan Talukder, Michael Kirley, Rajkumar Buyya
• Keywords: Computational Cost, Function Evaluation, GECCO 2008, Multiobjective Evolutionary Algorithms, Optimization