The last chapter of the three-part series on Evolutionary Feature Selection with Big datasets. We will address some fundamental design aspects of a Genetic Algorithm (GA) and commonly chosen options, to then move on to the CHC algorithm and a distributed approach for Feature Selection.
This is the second chapter of a three-part series on Evolutionary Feature Selection with Big datasets. We will start where we left off, namely with a review of existing metaheuristics with special focus on Genetic Algorithms.
When we want to fit a Machine Learning (ML) model to a big dataset, it is often recommended to carefully pre-process the input data in order to obtain better results.
This post will focus on a class of metaheuristics known as Swarm Intelligence. The most amazing feature of these algorithms is their ability to solve complex problems by a set of cooperative agents posing very simple intelligence.