Integrating Exploratory Landscape Analysis into Metaheuristic Algorithms

The no free lunch (NFL) theorem puts a limit to the range of problems a certain metaheuristic algorithm can be applied to successfully. For many methods these limits are unknown a priori and have to be discovered by experimentation. With the use of fitness landscape analysis (FLA) it is possible to obtain characteristic data and understand why methods perform better than others. In past research this data has been gathered mostly by a separate set of exploration algorithms. In this work it is studied how FLA methods can be integrated into the metaheuristic algorithm. We present a new exploratory method for obtaining landscape features that is based on path relinking (PR) and show that this characteristic information can be obtained faster than with traditional sampling methods. Path relinking is used in several metaheuristic which creates the possibility of integrating these features and enhance algorithms to output landscape analysis in addition to good solutions.