- author = "J. J. Valdes and A. J. Barton",
- title = "Visualizing High Dimensional Objective Spaces for Multi-objective Optimization: A Virtual Reality Approach",
- booktitle = "2007 IEEE Congress on Evolutionary Computation",
- year = "2007",
- editor = "Dipti Srinivasan and Lipo Wang",
- pages = "4199--4206",
- address = "Singapore",
- month = "25-28 " # sep,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- ISBN = "1-4244-1340-0",
- file = "1535.pdf",
- keywords = "genetic algorithms, genetic programming, Pareto optimisation, data mining, data visualisation, knapsack problems, virtual reality4 dimensional knapsack problem, Pareto fronts, high dimensional objective spaces, multi-objective evolutionary algorithms, multi-objective optimization, virtual reality, visual representations",
-
DOI = "
doi:10.1109/CEC.2007.4425019",
- abstract = "This paper presents an approach for constructing 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 which are difficult to use. 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 expected characteristics of the high dimensional fronts in terms of relative sizes, sequencing, embedding and asymmetry were systematically observed in the constructed virtual reality spaces.",
-
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C Also known as \cite{4425019}",
