  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 past hill-climb
experience to extract BB information and adapts its neighborhood structure
accordingly. The perpetual adaptation of the neighborhood structure allows the
method to climb the hierarchical structure solving successively 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.
