Virtual Reality High Dimensional Objective Spaces for Multi-Objective Optimization: An Improved Representation

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@InProceedings{Valdes:2007:cec,
  author =       "Julio J. Valdes and Alan J. Barton and 
                 Robert Orchard",
  title =        "Virtual Reality High Dimensional Objective Spaces for
                 Multi-Objective Optimization: An Improved
                 Representation",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "4191--4198",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1796.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4425018",
  abstract =     "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 optimisation
                 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 minimising 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, and VEGA. The
                 improved representation captures more accurately the
                 real nature of the m-dimensional objective spaces and
                 the quality of the mappings obtained with genetic
                 programming is equivalent to those computed with
                 classical optimization algorithms.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",
}

Genetic Programming entries for Julio J Valdes Alan J Barton Robert Orchard

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