定义了基于排列的染色体的局部极值，并以此为基础构造了求极值的爬山算法。

Local optimum of the permutation-based chromosomes is defined, and a hill-climbing algorithm is proposed to get the local optimum.

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根据机器人的运动模型构造了模糊控制器，采用变长度编码方法对规则编码，减少了染色体的尺寸和复杂度，提高了学习速度。

The fuzzy controller is constructed based on the kinematic model of the robot. Messy coding method is used to reduce the size and complexity of chromosome and increase the learning speed.

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该算法采用量子比特概率编码方式构造染色体，由量子旋转门操作实现种群进化。

This algorithm codes the chromosomes in the way of quantum bit probability, and makes the population evolve by the operation of quantum gate.

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