Performance of evolutionary algorithms on NK landscapes with nearest neighbor interactions and tunable overlap

This paper presents a class of NK landscapes with nearest-neighbor interactions and tunable overlap. The considered class of NK landscapes is solvable in polynomial time using dynamic programming; this allows us to generate a large number of random problem instances with known optima. Several genetic and evolutionary algorithms are then applied to the generated problem instances. The results are analyzed and related to scalability theory for genetic algorithms and estimation of distribution algorithms.