Improved adaptive genetic algorithm and its application to image segmentation

Lei Wang; Tingzhi Shen

doi:10.1117/12.441434

21 September 2001 Improved adaptive genetic algorithm and its application to image segmentation

Lei Wang, Tingzhi Shen

Proceedings Volume 4550, Image Extraction, Segmentation, and Recognition; (2001) https://doi.org/10.1117/12.441434
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

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, fit_max, fit_min, and fit_ave 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 p_c and p_m 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.

Citation Download Citation

Lei Wang and Tingzhi Shen "Improved adaptive genetic algorithm and its application to image segmentation", Proc. SPIE 4550, Image Extraction, Segmentation, and Recognition, (21 September 2001); https://doi.org/10.1117/12.441434

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