Large‐scale data mining using genetics‐based machine learning

In the last decade, genetics‐based machine learning methods have shown their competence in large‐scale data mining tasks because of the scalability capacity that these techniques have demonstrated. This capacity goes beyond the innate massive parallelism of evolutionary computation methods by the proposal of a variety of mechanisms specifically tailored for machine learning tasks, including knowledge representations that exploit regularities in the datasets, hardware accelerations or data‐intensive computing methods, among others. This paper reviews different classes of methods that alone or (in many cases) combined accelerate genetics‐based machine learning methods.