Optimization Algorithm of Evolutionary Design of Circuits Based on Genetic Algorithm

被引：4

作者：

Song, Xuejun ^{[1
]}

Cui, Yanli ^{[1
]}

Li, Aiting ^{[1
]}

机构：

[1] Hebei Normal Univ, Coll Phys Sci & Informat Engn, Shijiazhuang, Peoples R China

来源：

2012 FIFTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2012), VOL 1 | 2012年

关键词：

Evolutionary Design of Circuits; Fitness Evaluation; Genetic Algorithm; Optimization Methods;

D O I：

10.1109/ISCID.2012.91

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

For the convergence speed and scale bottlenecks of evolutionary design of circuits, the paper explores a new evolutionary method on the basis of genetic algorithm. Several optimization methods including fitness sharing, exponential weighting, double selection population, "Queen bee" mating, module crossover and optimal solution set are proposed to improve genetic algorithm. The new algorithm improved fitness evaluation method and genetic strategies. The experiment shows that the new evolutionary algorithm accelerates evolution convergence greatly, improves the adaptability effectively and expands the scale of evolved circuit obviously.

引用

页码：336 / 339

页数：4

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