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Proceedings Paper

Improved adaptive genetic algorithm and its application to image segmentation
Author(s): Lei Wang; Tingzhi Shen
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Paper Abstract

Genetic Algorithm (GA) is derived from the mechanics of genetic adaptation in biological systems, which can search the global space of certain application effectively. The proposed algorithm introduces three parameters, fitmax, fitmin, and fitave to measure how close the individuals are, so as to improve the Adaptive Genetic Algorithm (AGA) proposed by M. Sriniras. At the same time, the elitist strategy is employed to protect the best individual of each generation, and Remainder Stochastic Sampling with Replacement (RSSR) is employed in the proposed Improved Adaptive Genetic Algorithm (IAGA) to improve the basic reproduction operator. The proposed IAGA is applied to image segmentation. The experimental results exhibit satisfactory segmentation and demonstrate the learning capabilities of it. By determining pc and pm of the whole generation adaptively, it strikes a balance between the two incompatible goals: sustain the global convergence capacity and converge rapidly to global optimum.

Paper Details

Date Published: 21 September 2001
PDF: 6 pages
Proc. SPIE 4550, Image Extraction, Segmentation, and Recognition, (21 September 2001); doi: 10.1117/12.441434
Show Author Affiliations
Lei Wang, Beijing Institute of Technology (China)
Tingzhi Shen, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 4550:
Image Extraction, Segmentation, and Recognition
Tianxu Zhang; Bir Bhanu; Ning Shu, Editor(s)

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