GitHub - M-Haertling/CMOS-GA-Optimizer: A group research project intended to explore the use of genetic algorithms in optimizing CMOS circuits. The genetic algorithm is supplemented with a simulated annealing hybrid that allows a temperature to control the intensity of the evolution process. A high temperature allows inferior mutations to occur. The lower the temperature, the less likely it is that inferior mutations will be allowed into the next population. Adjusting this temperature value over the course of the simulation allows the population to explore the solution space more thoroughly before optimization begins. This reduces the likelihood of being stuck in a local maxima that is less optimal than some other maxima. SPICE, a common electrical simulation tool, was used to perform the simulations. This algorithm was also tested on the optimization of neural networks. The corresponding code is included.

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src/genetic_algorithms		src/genetic_algorithms
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build.xml		build.xml
manifest.mf		manifest.mf

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A group research project intended to explore the use of genetic algorithms in optimizing CMOS circuits. The genetic algorithm is supplemented with a simulated annealing hybrid that allows a temperature to control the intensity of the evolution process. A high temperature allows inferior mutations to occur. The lower the temperature, the less likel…

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