316 - Optimizing Steel Strength using Metaheuristic algorithms (e.g., Genetic)
Code generated in the video can be downloaded from here:
https://github.com/bnsreenu/python_fo...
In this example, we will work with the steel alloy data set.
Download from here: https://www.kaggle.com/datasets/fuarr...
The data set contains the elemental composition of different alloys and their respective yield and tensile strengths.
A machine learning model can be trained on this data, allowing us to predict the strength of an alloy based on its chemical composition.
But, for this exercise, let us try to find the optimized alloy composition with the best yield strength.
Let us explore metaheuristic approaches, especially the genetic algorithm and the differential evolution algorithm.
Note: Differential evolution (DE) is quite similar to the genetic algorithm (GA) with a few differences. DE relies on the distance and directional information through unit vectors for reproduction. Also, in DE, the crossover is applied after mutation unlike GA. In addition, the mutation operator is not created from a probability distribution, but from the creation of the unit vector.
