Genetic-based machine learning (GBML) systems, which employ evolutionary algorithms (EAs) as search mechanisms, evolve rule-based classification models to represent target concepts. Compared to Michigan-style GBML, Pittsburgh-style GBML is expected …
Pittsburgh-style learning classifier systems (LCSs), in which an entire candidate solution is represented as a set of variable number of rules, combine supervised learning with genetic algorithms (GAs) to evolve rule-based classification models. It …
This paper deals with the adaptive variance scaling issue incontinuous Estimation of Distribution Algorithms. A phenomenon is discovered that current adaptive variance scaling method in EDA suffers from imprecise structure learning. A new type of …