We proposed a new search heuristic using the scuba diving metaphor. This approach is based on the concept of evolvability and tends to exploit neutrality which exists in many real-world problems. Despite the fact that natural evolution does not directly select for evolvability, the basic idea behind the scuba search heuristic is to explicitly push evolvability to increase. A comparative study of the scuba algorithm and standard local search heuristics has shown the advantage and the limitation of the scuba search. In order to tune neutrality, we use the NKq fitness landscapes and a family of travelling salesman problems (TSP) where cities are randomly placed on a lattice and where travel distance between cities is computed with the Manhattan metric. In this last problem the amount of neutrality varies with the city concentration on the grid ; assuming the concentration below one, this TSP reasonably remains a NP-hard problem.