Scalable continuous multiobjective optimization with a neural network-based estimation of distribution algorithm

Abstract

To achieve a substantial improvement of MOEDAs regarding MOEAs it is necessary to adapt their model building algorithm to suit this particular task. Most current model building schemes used so far off-the-shelf machine learning methods. However, the model building problem has specific requirements that those methods do not meet and even avoid. In this we work propose a novel approach to model building in MOEDAs using an algorithm custom-made for the task. We base our proposal on the growing neural gas (GNG) network. The resulting model-building GNG (MB-GNG) is capable of yielding good results when confronted to high-dimensional problems. © 2008 Springer-Verlag Berlin Heidelberg.