A frequent pattern mining algorithm for understanding genetic algorithms

Abstract

In this paper, we present a Frequent Schemas Analysis (FSA) approach as an instance of Optinformatics for extracting knowledge on the search dynamics of Binary GA using the optimization data generated during the search. The proposed frequent pattern mining algorithm labeled here as LoFIA in FSA effectively mines for interesting implicit frequent schemas. Subsequently these schemas may be visualized to provide new insights into the workings of the search algorithm. A case study using the Royal Road problem is used to explain the search performance of Genetic Algorithm (GA) based on FSA in action. © 2008 Springer-Verlag Berlin Heidelberg.