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首页> 外文会议>Conference on Image Extraction, Segmentation, and Recognition Oct 22-24, 2001, Wuhan, China >An improved adaptive genetic algorithm and its application to image segmentation
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An improved adaptive genetic algorithm and its application to image segmentation

机译：改进的自适应遗传算法及其在图像分割中的应用

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摘要

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".
机译：遗传算法（GA）源自生物系统中遗传适应的机制，可以有效地搜索某些应用程序的全局空间。该算法引入了三个参数：fit_（max），fit_（min）和fit_（ave）来测量个体的接近程度，从而改进了M. Sriniras提出的自适应遗传算法（AGA）。同时，采用精英策略来保护每一代的最佳个体，并在提出的改进的自适应遗传算法（IAGA）中采用置换的剩余随机抽样（RSSR）来改进基本的繁殖算子。提出的IAGA应用于图像分割。实验结果显示出令人满意的分割效果，并证明了其学习能力。通过自适应地确定整个世代的p_c和p_m，它在两个不兼容的目标之间取得了平衡：“维持全局收敛能力”和“快速收敛至全局最优”。

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