Advancing model–building for many–objective optimization estimation of distribution algorithms

Authors:
Luis Martí;Jesús García;Antonio Berlanga;José M. Molina
Affiliations:
Group of Applied Artificial Intelligence, Universidad Carlos III de Madrid, Madrid, Spain;Group of Applied Artificial Intelligence, Universidad Carlos III de Madrid, Madrid, Spain;Group of Applied Artificial Intelligence, Universidad Carlos III de Madrid, Madrid, Spain;Group of Applied Artificial Intelligence, Universidad Carlos III de Madrid, Madrid, Spain
Venue:
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Year:
2010

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Abstract

In order to achieve a substantial improvement of MOEDAs regarding MOEAs it is necessary to adapt their model–building algorithms. Most current model–building schemes used so far off–the–shelf machine learning methods. These methods are mostly error–based learning algorithms. However, the model–building problem has specific requirements that those methods do not meet and even avoid. In this work we dissect this issue and propose a set of algorithms that can be used to bridge the gap of MOEDA application. A set of experiments are carried out in order to sustain our assertions.