Circuit Design Optimization Using Genetic Algorithm with Parameterized Uniform Crossover

Zhiguo BAO  Takahiro WATANABE  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E93-A   No.1   pp.281-290
Publication Date: 2010/01/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E93.A.281
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Nonlinear Problems
genetic algorithm,  evolutionary algorithms,  circuit optimization,  evolutionary circuit design,  evolvable hardware,  

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Evolvable hardware (EHW) is a new research field about the use of Evolutionary Algorithms (EAs) to construct electronic systems. EHW refers in a narrow sense to use evolutionary mechanisms as the algorithmic drivers for system design, while in a general sense to the capability of the hardware system to develop and to improve itself. Genetic Algorithm (GA) is one of typical EAs. We propose optimal circuit design by using GA with parameterized uniform crossover (GApuc) and with fitness function composed of circuit complexity, power, and signal delay. Parameterized uniform crossover is much more likely to distribute its disruptive trials in an unbiased manner over larger portions of the space, then it has more exploratory power than one and two-point crossover, so we have more chances of finding better solutions. Its effectiveness is shown by experiments. From the results, we can see that the best elite fitness, the average value of fitness of the correct circuits and the number of the correct circuits of GApuc are better than that of GA with one-point crossover or two-point crossover. The best case of optimal circuits generated by GApuc is 10.18% and 6.08% better in evaluating value than that by GA with one-point crossover and two-point crossover, respectively.