Abstract
A hybrid genetic algorithm combined with split and merge techniques (SMGA) is proposed for two types of polygonal approximation of digital curve, i.e. Min-# problem and Min-ε Problem. Its main idea is that two classical methods—split and merge techniques are applied to repair infeasible solutions. In this scheme, an infeasible solution can not only be repaired rapidly, but also be pushed to a local optimal location in the solution space. In addition, unlike the existing genetic algorithms which can only solve one type of polygonal approximation problem, SMGA can solve two types of polygonal approximation problems. The experimental results demonstrate that SMGA is robust and outperforms other existing GA-based methods.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, B., Shi, C. (2006). A Hybrid Genetic Algorithm for Two Types of Polygonal Approximation Problems. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Sciences, vol 345. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37258-5_4
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DOI: https://doi.org/10.1007/978-3-540-37258-5_4
Publisher Name: Springer, Berlin, Heidelberg
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