Dimensionality Reduction is the process of reducing the number of variables or features in a dataset while retaining its essential information. By eliminating irrelevant or redundant features, businesses can simplify data analysis, improve model performance, and reduce computational complexity. Dimensionality Reduction techniques include Principal Component Analysis (PCA) and t-SNE.
Neuroevolution is a type of AI learning that combines neural networks and evolutionary algorithms. Neuroevolution algorithms evolve neural networks over generations, adapting them to…