NP-hard problems in hierarchical-tree clustering
Acta Informatica
Multiobjective optimization with messy genetic algorithms
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Innovization: innovating design principles through optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Towards billion-bit optimization via a parallel estimation of distribution algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Solving hierarchical optimization problems using MOEAs
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
Radar waveform optimisation as a many-objective application benchmark
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Pareto set and EMOA behavior for simple multimodal multiobjective functions
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Automated innovization for simultaneous discovery of multiple rules in bi-objective problems
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Design knowledge extraction in multi-objective optimization problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
GECCO 2012 tutorial on evolutionary multiobjective optimization
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
GECCO 2013 tutorial on evolutionary multiobjective optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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In a multiobjective setting, evolutionary algorithms can be used to generate a set of compromise solutions. This makes decision making easier for the user as he has alternative solutions at hand which he can directly compare. However, if the number of solutions and the number of decision variables which define the solutions are large, such an analysis may be difficult and corresponding tools are desirable to support a human in separating relevant from irrelevant information. In this paper, we present a method to extract structural information from Pareto-set approximations which offers the possibility to present and visualize the trade-off surface in a compressed form. The main idea is to identify modules of decision variables that are strongly related to each other. Thereby, the set of decision variables can be reduced to a smaller number of significant modules. Furthermore, at the same time the solutions are grouped in a hierarchical manner according to their module similarity. Overall, the output is a dendrogram where the leaves are the solutions and the nodes are annotated with modules. As will be shown on knapsack problem instances and a network processor design application, this method can be highly useful to reveal hidden structures in compromise solution sets.