  In a standard NP-complete optimization problem we introduce an interpolating
algorithm between the quick decrease along the gradient (greedy dynamics) and a
slow decrease close to the level curves (reluctant dynamics). We find that for
a fixed elapsed computer time the best performance of the optimization is
reached at a special value of the interpolation parameter, considerably
improving the results of the pure cases greedy and reluctant.
