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Bell-Curve Based Evolutionary Optimization Algorithm

机译:基于Bell-Curve的进化优化算法

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摘要

The paper presents an optimization algorithm that falls in the category of genetic, or evolutionary algorithms. While the bit exchange is the basis of most of the Genetic Algorithms (GA) in research and applications in America, some alternatives, also in the category of evolutionary algorithms, but use a direct, geometrical approach have gained popularity in Europe and Asia. The Bell-Curve Based Evolutionary Algorithm (BCB) is in this alternative category and is distinguished by the use of a combination of n-dimensional geometry and the normal distribution, the bell-curve, in the generation of the offspring. The tool for creating a child is a geometrical construct comprising a line connecting two parents and a weighted point on that line. The point that defines the child deviates from the weighted point in two directions: parallel and orthogonal to the connecting line, the deviation in each direction obeying a probabilistic distribution. Tests showed satisfactory performance of BCB. The principal advantage of BCB is its controllability via the normal distribution parameters and the geometrical construct variables.
机译:本文提出了一种属于遗传算法或进化算法的优化算法。尽管位交换是美国大多数遗传算法(GA)研究和应用的基础,但在进化算法类别中也有一些替代方法,但使用直接的几何方法已在欧洲和亚洲流行。基于Bell-Curve的进化算法(BCB)在此替代类别中,其后代在生成后代时使用了n维几何形状和正态分布(Bell曲线)的组合。创建孩子的工具是一种几何构造,包括连接两个父代的线和该线上的加权点。定义孩子的点在两个方向上偏离加权点:平行和正交于连接线,每个方向上的偏差服从概率分布。测试表明,BCB的性能令人满意。 BCB的主要优点是可通过正态分布参数和几何构造变量进行控制。

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