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Design and Optimization of Digital Circuits by Artificial Evolution Using Hybrid Multi Chromosome Cartesian Genetic Programming

机译:混合多染色体笛卡尔遗传算法通过人工进化设计和优化数字电路

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摘要

Traditional digital circuit design techniques are based, for the most part, on top-down methods, which use a set of rules and restrictions to assist the construction of the project. Genetic algorithms, on the other hand, haven proven themselves to be a very useful tool for solving high complexity problems, relying on a bottom-up methodology. This paper proposes a new design algorithm, named HMC-CGP, which operates by first finding a functional solution by using the MC-CGP method. Then, optimizes it by using standard CGP approach. Test circuits used include 1 and 2 bit full adders, 2 bit multiplier and 7 segment hexadecimal decoder. Obtained results show that by making use of faster convergence granted by the MC-CGP mechanism together with an optimization strategy generates novel approaches for those circuits, with results showing a logic gate and transistor usage reduction of up to 60.8 %.
机译:传统的数字电路设计技术主要基于自上而下的方法,该方法使用一组规则和限制来协助项目的建设。另一方面,遗传算法依靠自下而上的方法证明自己是解决高复杂性问题的非常有用的工具。本文提出了一种新的设计算法,称为HMC-CGP,该算法首先通过使用MC-CGP方法找到一个功能解决方案来进行操作。然后,使用标准CGP方法对其进行优化。所用的测试电路包括1位和2位全加法器,2位乘法器和7段十六进制解码器。所得结果表明,通过利用MC-CGP机制所赋予的更快收敛性以及一种优化策略,可以为这些电路产生新颖的方法,结果表明,逻辑门和晶体管的使用最多减少了60.8%。

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