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Macro-cell and module placement by genetic adaptive search with bitmap-represented chromosome

机译:宏观小区和模块通过基因自适应搜索与位图代表染色体的校正

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摘要

The genetic algorithm has been applied to the VLSI module placement problem. This algorithm is an iterative, evolutional approach. A placement configutation is represented by a set of primitive features such as location and orientation, and the features are arranged in the form of a two-dimensional bitmap chromosome. The representation is flexible, and can handle arbitrarily shaped cells, and pads, and is applicable to the placement of macro cells, and gate arrays. Three new versions of genetic operators, namely, crossover, inversion and mutation, are used to explore the solution space. Crossover creates new configurations by combining attributes from a pair of existing configurations. This feature passing scheme constitutes the primary difference between our genetic approach and the other traditional searching techniques. Inversion enables more uniform inheritance of features from one generation to the next, and mutation prevents the algorithm from getting trapped at local optima. We have pointed out that the bitmap representation enables the algorithm to divide the entire solution space into a set of feature-equivalent classes, or schemata where each class contains a set of solutions with common physical attributes. We show that the genetic algorithm adaptively biases the search based on the past observed fitness of the schemata. We also demonstrated the power of the genetic algorithm experimentally for macro cell placement, and obtained satisfactory results.
机译:遗传算法已应用于VLSI模块放置问题。该算法是一种迭代的进化方法。放置配置由诸如位置和方向的一组原始特征表示,并且特征以二维位图染色体的形式排列。表示是灵活的,并且可以处理任意形状的细胞和垫,并且适用于宏小区的放置和栅极阵列。三种新版本的遗传算子,即交叉,反演和突变,用于探索解决方案空间。交叉通过组合来自一对现有配置的属性来创建新配置。该特征通过方案构成了我们的遗传方法与其他传统搜索技术之间的主要差异。反转使得能够更统一的特征继承到下一个一代,并且突变可防止算法被捕获在本地最佳状态。我们已经指出,位图表示使算法能够将整个解决方案空间划分为一组特征等效类,或者每个类包含具有常见物理属性的一组解决方案的模式。我们表明遗传算法基于过去观察到的模式的适应性,自适应地偏置了搜索。我们还通过实验实验展示了遗传算法的力量,并获得了令人满意的结果。

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