Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping

D. Balakrishnan; C. Quan; C. J. Tay

doi:10.1117/12.2020382

22 June 2013 Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping

D. Balakrishnan, C. Quan, C. J. Tay

Proceedings Volume 8769, International Conference on Optics in Precision Engineering and Nanotechnology (icOPEN2013); 87691N (2013) https://doi.org/10.1117/12.2020382
Event: International Conference on Optics in Precision Engineering and Nanotechnology (icOPEN2013), 2013, Singapore, Singapore

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.

Citation Download Citation

D. Balakrishnan, C. Quan, and C. J. Tay "Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping", Proc. SPIE 8769, International Conference on Optics in Precision Engineering and Nanotechnology (icOPEN2013), 87691N (22 June 2013); https://doi.org/10.1117/12.2020382

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available