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in AI and Deep Learning by (50.2k points)

I know that my question is general, but I'm new to the AI area. I have an experiment with some parameters (almost 6 parameters). Each one of them is an independent one, and I want to find the optimal solution for maximum or minimum the output function. However, if I want to do it in a traditional programming technique it will take much time since i will use six nested loops.

I just want to know which AI technique to use for this problem? Genetic Algorithm? Neural Network? Machine learning?


The problem could have more than one evaluation function. It will have one function that we should minimize it (Cost) and another function we want to maximize it (Capacity) Maybe another function can be added. Example: Construction a glass window can be done in a million ways. However, we want the strongest window at the lowest cost. Many parameters affect the pressure capacity of the window such as the strength of the glass, Height and Width, slope of the window. If we go to extreme cases (Largest strength glass, with smallest width and height, and zero slopes) the window will be extremely strong. However, the cost for that will be very high.

I want to study the interaction between the parameters in a specific range.

1 Answer

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by (108k points)

It sounds like Genetic Algorithms would be perfect. They've been used a lot for parameter optimization and have often given good results. They can be used to narrow parameter ranges for edge detection techniques with about 15 variables.Having multiple evaluation functions needn't be a problem if you code this into the Genetic Algorithm's fitness function. You can look up multi-objective optimization with genetic algorithms.

You can also refer to the following link for more information regarding the use of artificial intelligence technology for the optimization of process parameters used in the continuous casting of steel:

If you want to learn more about the Genetic Algorithm then visit this Artificial Intelligence Course.

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