On the Eeectiveness of Evolutionary Search in High{dimensional Nk{landscapes

| NK{landscapes ooer the ability to assess the performance of evolutionary algorithms on problems with different degrees of epistasis. In this paper, we study the performance of six algorithms in NK{landscapes with low and high dimension while keeping the amount of epistatic interactions constant. The results show that compared to genetic local search algorithms, the performance of standard genetic algorithms employing crossover or mutation signiicantly decreases with increasing problem size. Furthermore, with increasing K , crossover based algorithms are in both cases out-performed by mutation based algorithms. However, the relative performance diierences between the algorithms grow signiicantly with the dimension of the search space, indicating that it is important to consider high{dimensional landscapes for evaluating the performance of evolutionary algorithms .