Publication:
Multiobjective Local Search Techniques for Evolutionary Polygonal Approximation

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URI: https://hdl.handle.net/10016/21676
ISSN: 2194-5357
ISBN: 978-3-319-00550-8 (print)
ISBN: 978-3-319-00551-5 (online)
DOI: 10.1007/978-3-319-00551-5_21
UXXI: CC/0000021633
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multiobjective_DCAI_2010_ps.pdf (446.26 KB)
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2013
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Springer
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Abstract
Polygonal approximation is based on the division of a closed curve into a set of segments. This problem has been traditionally approached as a single-objective optimization issue where the representation error was minimized according to a set of restrictions and parameters. When these approaches try to be subsumed into more recent multi-objective ones, a number of issues arise. Current work successfully adapts two of these traditional approaches and introduces them as initialization procedures for a MOEA approach to polygonal approximation, being the results, both for initial and final fronts, analyzed according to their statistical significance over a set of traditional curves from the domain.
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Proceedings of: 10th International Symposium on Distributed Computing and Artificial Intelligence . University of Salamanca (DCAI 2013). Salamanca, Spain, Spain, May 22-24, 2013.
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Polygonal approximation, Multi-Objective Evolutionary Algorithms, Pareto-optimal front, Evolutionary algorithms
Bibliographic citation
Omatu, S., et al. (eds.) Distributed Computing and Artificial Intelligence: 10th International Conference. (pp. 171-178). (Advances in Intelligent Systems and Computing; 217). Springer International Publishing.
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