This talk will describe our Evolutionary Rule Discovery system, EvRFind.

EvRFind is a hybrid Genetic Algorithm (GA) based solution that employs other techniques from statistics and machine learning to improve the efficiency and performance of the search. Among the non-evolutionary components are algorithms such as Hill Climbing, optimization methods designed to improve search speed, automatic concept generalization, and automatic expansion of the description language.

To demonstrate the expressive power of EvRFind the Poker Hand Dataset will also be described. This dataset represents a very large, imbalanced, and challenging domain. The description of the Poker Hand dataset is followed by an analysis of the results generated by EvRFind. The results are also compared to those generated by several other machine learning algorithms.