Solving Non-Linear Problems with Genetic Algorithms (Part 2)
We first explored a very simple methodology, which consisted of applying the largest possible number of solutions (= individuals) to the non-linear model and picking the best ones.
However, this method might require a lot of computation power and time as the number of features and complexity level of the model increase.
Before we jump into permutations and mutations (which are the traditional methods used), I would like to offer an alternative technique I am using with good results.
However, please keep in mind that I am using this optimization technique for industry modeling problems and it could (probably) fail in other areas.

#python #genetic-algorithm #non-linear #machine-learning #programming