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Erschienen in:
2013 | OriginalPaper | Buchkapitel
A Local Elitism Based Membrane Evolutionary Algorithm for Point Pattern Matching
verfasst von : Zhuanlian Ding, Jin Tang, Xingyi Zhang, Bin Luo
Erschienen in: Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013
Verlag: Springer Berlin Heidelberg
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Abstract
Point pattern matching is a fundamental problem in computer vision and pattern recognition. Membrane computing is an emergent branch of bio-inspired computing, which provides a novel idea to solve computationally hard problems. In this paper, a new point pattern matching algorithm with local elitism strategy is proposed based on membrane computing models. Local elitism strategy is used to keep good correspondences of point pattern matching found during the search, so the matching rate and the convergence speed are improved. Five heuristic mutation rules are introduced to avoid the local optimum. Experiment results on both synthetic data and real world data illustrate that the proposed algorithm is of higher matching rate and better stability.