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Development of an adaptive learning type optimization technique based on evolutionary systems and application to VLSI layout design

基于进化系统的自适应学习型优化技术的开发及其在VLSI版图设计中的应用

基本信息

批准号：
18560399
负责人：
KOAKUTSU Seiichi
金额：
$ 1.24万
依托单位：
Chiba University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18560399/
关键词：
Systems Engineering Soft Computing Systems Optimization Placement and Routing Problem

项目摘要

This research aims at a development of an adaptive learning type optimization technique that adjusts its search process based on the structure of a cost function. The developed technique is applied to combinatorial optimization problems such as placement and wiring problems of VLSI which have a large-scale and complex cost function. In the developed technique, information on the structure of the cost function is collected in the process of optimization, and it is used to adjust the search process of the solution. The developed technique is effective to quality improvement of the solution and shortening at computation time. The developed technique is based on Genetic Algorithms that imitate biological evolution. In Genetic Algorithms, it is possible to search for the solution of high quality efficiently by encoding the state of the solution in gene, and combining profitable partial solutions by using crossover operators. However, when applying Genetic Algorithms to practical problems, it is not easy to find an ideal gene coding and crossover operators. The feature of the developed technique is to encode the search process in gene, and to introduce the function to optimize the search process according to the feature of the observed cost function into Genetic Algorithms. As a result, the solution of the high quality comes to be obtained efficiently without adjusting the gene coding and the crossover operators of each problem by hand, and without depending on the scale and modeling of problems.

本研究旨在开发一种自适应学习型优化技术，该技术根据成本函数的结构调整其搜索过程。所开发的技术应用于组合优化问题，例如具有大规模且复杂成本函数的VLSI布局和布线问题。在所开发的技术中，在优化过程中收集有关成本函数结构的信息，并用于调整解决方案的搜索过程。所开发的技术可有效提高解的质量并缩短计算时间。开发的技术基于模仿生物进化的遗传算法。在遗传算法中，通过将解的状态编码在基因中，并使用交叉算子组合有利可图的部分解，可以有效地搜索高质量的解。然而，将遗传算法应用于实际问题时，找到理想的基因编码和交叉算子并不容易。该技术的特点是将搜索过程编码在基因中，并将根据观察到的成本函数的特征来优化搜索过程的功能引入到遗传算法中。因此，无需手动调整每个问题的基因编码和交叉算子，也不依赖于问题的规模和建模，就能高效地获得高质量的解决方案。