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2007
IEEE

Virtual reality high dimensional objective spaces for multi-objective optimization: An improved representation

8 years 11 months ago
Virtual reality high dimensional objective spaces for multi-objective optimization: An improved representation
This paper presents an approach for constructing improved visual representations of high dimensional objective spaces using virtual reality. These spaces arise from the solution of multi-objective optimization problems with more than 3 objective functions which lead to high dimensional Pareto fronts. The 3-D representations of m-dimensional Pareto fronts, or their approximations, are constructed via similarity structure mappings between the original objective spaces and the 3-D space. Alpha shapes are introduced for the representation and compared with previous approaches based on convex hulls. In addition, the mappings minimizing a measure of the amount of dissimilarity loss are obtained via genetic programming. This approach is preliminarily investigated using both theoretically derived high dimensional Pareto fronts for a test problem (DTLZ2) and practically obtained objective spaces for the 4 dimensional knapsack problem via multi-objective evolutionary algorithms like HLGA, NSGA, ...
Julio J. Valdés, Alan J. Barton, Robert Orc
Added 12 Aug 2010
Updated 12 Aug 2010
Type Conference
Year 2007
Where CEC
Authors Julio J. Valdés, Alan J. Barton, Robert Orchard
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