Overcoming hierarchical difficulty by hill-climbing the building block structure

The Building Block Hypothesis suggests that Genetic Algorithms (GAs) are well-suited for hierarchical problems, where efficient solving requires proper problem decomposition and assembly of solution from sub-solution with strong non-linear interdependencies. The paper proposes a hill-climber operating over the building block (BB) space that can efficiently address hierarchical problems. The new Building Block Hill-Climber (BBHC) uses hill-climb search experience to learn the problem structure. The neighborhood structure is adapted whenever new knowledge about the underlaying BB structure is incorporated into the search. This allows the method to climb the hierarchical structure by revealing and solving consecutively the hierarchical levels. It is expected that for fully non-deceptive hierarchical BB structures the BBHC can solve hierarchical problems in linearithmic time. Empirical results confirm that the proposed method scales almost linearly with the problem size thus clearly outperforms population based recombinative methods.