Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Robust growing neural gas algorithm with application in cluster analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Advances in Evolutionary Algorithms: Theory, Design and Practice (Studies in Computational Intelligence)
No free lunch and free leftovers theorems for multiobjective optimisation problems
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
The naive MIDEA: a baseline multi-objective EA
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
IEEE Transactions on Evolutionary Computation
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Advancing model–building for many–objective optimization estimation of distribution algorithms
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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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 tomodel 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.