We have implemented and benchmarked an FPT-algorithm, that has two input parameters, k and w besides the input problem instance, which is a planing instance, in this thesis. The algorithm has an exponential running time as a function of these two parameters. The implemented algorithm computes the heuristic value h^+(s) of a state s that belongs to a state space, which originates from a strips instance. The purpose of the project was to test if the algorithm can be used to compute the heuristic function h^+, i.e. the delete-relaxation heuristic, in practice. The delete-relaxation heuristic value for some state is the length of the optimal solution from the state to a goal in the delete-relaxed-instance, which is the original instance without all its negative effects. Planning instances was benchmarked with the search algorithm A^* to test the algorithms practical value. The heuristic function blind was benchmarked together with A^* with the same instances so that we could compare the quality of the benchmark result for the implemented algorithm. The conclusion of the project was that the implemented algorithm is too slow to be used in practise.