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Proceedings Paper

Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping
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Paper Abstract

The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.

Paper Details

Date Published: 22 June 2013
PDF: 10 pages
Proc. SPIE 8769, International Conference on Optics in Precision Engineering and Nanotechnology (icOPEN2013), 87691N (22 June 2013); doi: 10.1117/12.2020382
Show Author Affiliations
D. Balakrishnan, National Univ. of Singapore (Singapore)
C. Quan, National Univ. of Singapore (Singapore)
C. J. Tay, National Univ. of Singapore (Singapore)


Published in SPIE Proceedings Vol. 8769:
International Conference on Optics in Precision Engineering and Nanotechnology (icOPEN2013)
Chenggen Quan; Kemao Qian; Anand Asundi, Editor(s)

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