The strongest schema learning GA and its application to multilevel thresholding

Authors:
Li Cao;Paul Bao;Zhongke Shi
Affiliations:
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Jiang Su 210016, China and Department of Information Technology, University of South Florida, FL 33803, USA;Department of Information Technology, University of South Florida, FL 33803, USA;Department of Automatic Control, Northwestern Polytechnical University, Shan Xi, 710072, China
Venue:
Image and Vision Computing
Year:
2008

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Abstract

The multilevel thresholding segmentation methods often outperform the bi-level methods. However, their computational complexity will also grow exponentially as the threshold number increases due to the exhaustive search. Genetic algorithms (GAs) can accelerate the optimization calculation but suffer drawbacks such as slow convergence and easy to trap into local optimum. Extracting from several highest performance strings, a strongest scheme can be obtained. With the low performance strings learning from it with a certain probability, the average-fitness of each generation can increase and the computational time will improve. On the other hand, the learning program can also improve the population diversity. This will enhance the stability of the optimization calculation. Experiment results showed that it was very effective for multilevel thresholding.