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. © Springer International Publishing Switzerland 2013.
CITATION STYLE
Guerrero, J. L., Berlanga, A., & Molina, J. M. (2013). Multiobjective local search techniques for evolutionary polygonal approximation. Advances in Intelligent Systems and Computing, 217, 171–178. https://doi.org/10.1007/978-3-319-00551-5_21
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