A recent trend in local search concerns the exploitation of several different
neighborhoods so as to increase the ability of the algorithm to navigate the search space. In this
work we investigate a hybridization technique, that we call Neighborhood Portfolio Approach, that
consists in the interleave of local search techniques based on various combinations of
neighborhoods. In particular, we are able to select the most effective search technique through a
systematic analysis of all meaningful combinations built upon a set of basic neighborhoods. The
proposed approach is applied to two practical problems belonging to the timetabling family, and
systematically tested and compared on real-world instances. The experimental analysis shows that
our approach leads to automatic design of new algorithms that provide better results than basic
local search techniques.
