Abstract
The inability to find the solution in engineering problems has led to a large part of the scientific community developing indirect and alternative techniques to find optimization problem-solving. Genetic algorithms are looking for models based on the natural and genetic selection process, which optimizes a population or set of possible solutions to deliver one that is optimal or at least very close to it in the sense of a fitting function. In this work, we derive and evaluate a method based on genetic algorithms to find the relative maximum of differentiable functions that are difficult to find by analytical methods. We build a library in Python that includes different components from genetic algorithms. The test problems include finding the maximum or minimum of functions in one and two dimensions.
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