Using the principles of genetics, to create and optimise circuit design solutions to provides problems
In this project, A system is provided with a problem, A set of input conditions of which a suitable circuit needs to be designed and optermised.
In many cases, a selection of methods can be Used to calculate a feasible solution to a circuit problem.
However, like trying every possible combination for the best answer, the more input variables introduced, the more computationally intensive this can become.
Can the use of Genetic Algorithms have anything to offer in this area?
Designing Digital Logic Circuits can be both a fun but aggravating task.
Truth tables and K-Maps can be used to easily produce Boolean Expressions that represent the functionality of a circuit, if not the circuit itself.
However, as the amounts of inputs increase, so does the complexity of the task. In many cases, problems with more than 6 variables are significantly harder to complete, compared to 4 variable problems, if not near impossible to be accomplished by hand.
Additionally, these methods may be able to find a solution, but are they necessarily the best solution?
If a problem was Brute-Forced, with every possible combination of logic gates evaluated, would a better solution be found?
This project won't exactly go into that but will attempt to find whether Genetic Algorithms, inspired by nature, can provide a beneficial resource to design, and possibly optimise a circuit design autonomously without the need of solving by hand or other calculation methods.
The image below displays an overview of what the system produces.
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