A preliminary study of learnable evolution methodology implemented with C4.5
Mark A. Coletti · 2003
The learnable evolution model (LEM) introduces a machine learning-based birth operator into an evolutionary computing algorithm. New individuals are generated from hypotheses learned by the operator from the most-fit and least-fit parent sub-populations. The LEM allows for arbitrary machine learning mechanisms, though, so far, only an AQ (Algorithm Quasi-optimal) based machine learner has been used in LEM implementations. This paper describes preliminary results using a different machine learner in a LEM implementation - C4.5.