A Study on Image Segmentation by an Improved Adaptive Algorithm

In the first place, an improvement was made on crossover and mutation of adaptive genetic algorithm (AGA) to let the crossover probability and mutation probability adapt nonlinearly. Then a comparison was made between Improved adaptive genetic algorithm (IAGA) and adaptive genetic algorithm (AGA) in...

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Bibliographic Details
Published in2007 International Conference on Machine Learning and Cybernetics Vol. 3; pp. 1570 - 1573
Main Authors Qing Li, Wen-Hao He, Han-Hong Jiang, Xuan-Zhong Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2007
Subjects
Adaptive algorithm
Convergence
Crossover
Cybernetics
Genetic algorithms
Genetic engineering
Genetic mutations
Helium
Image segmentation
Improved adaptive genetic algorithm (IAGA)
Machine learning
Mutation
Robustness
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ISBN1424409721
9781424409723
ISSN2160-133X
DOI10.1109/ICMLC.2007.4370395

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Summary:In the first place, an improvement was made on crossover and mutation of adaptive genetic algorithm (AGA) to let the crossover probability and mutation probability adapt nonlinearly. Then a comparison was made between Improved adaptive genetic algorithm (IAGA) and adaptive genetic algorithm (AGA) in segmentation time and adaptive function curve. The results indicated that IAGA can give attention to the main information of experiment images. And much less time was used by the algorithm. The process of searching for global optimum also became more stable than AGA.
ISBN:1424409721
9781424409723
ISSN:2160-133X
DOI:10.1109/ICMLC.2007.4370395