Genetic algorithms can be used for feature selection for machine learning algorithms. This article describes a very simple system where a '1' bit in the genome represents including the feature, and that's it -- everything after that is the standard crossover, mutation, and selection of genetic algorithms.

"Some advantages of genetic algorithms this method are the following: They usually perform better than traditional feature selection techniques, genetic algorithms can manage data sets with many features, they don't need specific knowledge about the problem under study, and these algorithms can be easily parallelized in computer clusters.

"And some disadvantages are: Genetic Algorithms might be very expensive in computational terms, since evaluation of each individual requires building a predictive model, and these algorithms can take a long time to converge, since they have a stochastic nature."
Shared publiclyView activity