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Square Function for Population Size in Quantum Evolutionary Algorithm and its Application in Fractal Image Compression

机译:量子进化算法中种群大小的方形功能及其在分形图像压缩中的应用

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Fractal Image Compression is a well-known problem which is in the class of NP-Hard problems. Quantum Evolutionary Algorithm is a novel optimization algorithm which uses a probabilistic representation for solutions and is highly suitable for combinatorial problems like Knapsack problem. Genetic algorithms are widely used for fractal image compression problems, but QEA is not used for this kind of problems yet. This paper improves QEA whit change population size and used it in fractal image compression. Experimental results show that our method have a better performance than GA and conventional fractal image compression algorithms.
机译:分形图像压缩是一个众所周知的问题,它是NP难题的类别。量子进化算法是一种新的优化算法,它使用概率表示解决方案,非常适合作为背包问题等组合问题。遗传算法广泛用于分形图像压缩问题,但QEA尚未用于这种问题。本文提高了QEA Whit含量尺寸,并在分形图像压缩中使用它。实验结果表明,我们的方法具有比GA和传统的分形图像压缩算法更好的性能。

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