First-Improvement vs. Best-Improvement Local Optima Networks of NK Landscapes
This paper extends a recently proposed model for combinatorial landscapes: Local Optima Networks (LON), to incorporate a first-improvement (greedy-ascent) hill-climbing algorithm, instead of a best-improvement (steepest-ascent) one, for the definition and extraction of the basins of attraction of the landscape optima. A statistical analysis comparing best and first improvement network models for a set of NK landscapes, is presented and discussed. Our results suggest structural differences between the two models with respect to both the network connectivity, and the nature of the basins of attraction. The impact of these differences in the behavior of search heuristics based on first and best improvement local search is thoroughly discussed.
KeywordsLocal Search Local Optimum Local Search Algorithm Short Path Length Stochastic Local Search
Unable to display preview. Download preview PDF.
- 2.Daolio, F., Verel, S., Ochoa, G., Tomassini, M.: Local optima networks of the quadratic assignment problem. In: Proceedings of the 2010 Congress on Evolutionary Computation, CEC 2010, 3145–3152 (2010)Google Scholar
- 4.Kauffman, S.A.: The Origins of Order. Oxford University Press, New York (1993)Google Scholar
- 7.Tomassini, M., Verel, S., Ochoa, G.: Complex-network analysis of combinatorial spaces: The NK landscape case. Phys. Rev. E 78(6), 066114 (2008)Google Scholar
- 8.Verel, S., Ochoa, G., Tomassini, M.: Local optima networks of NK landscapes with neutrality. IEEE Transactions on Evolutionary Computation (to appear)Google Scholar