Abstract
In this paper we present and investigate partial neighborhood local searches, which only explore a sample of the neighborhood at each step of the search. We particularly focus on establishing links between the structure of optimization problems and the efficiency of such local search algorithms. In our experiments we compare partial neighborhood local searches to state-of-the-art tabu search and iterated local search and perform a parameter sensitivity analysis by observing the efficiency of partial neighborhood local searches with different size of neighborhood sample. In order to facilitate the extraction of links between instances structure and search algorithm behavior we restrain the scope to binary fitness landscapes, such as NK landscapes and landscapes derived from UBQP.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Basseur, M., Goëffon, A.: Hill-climbing strategies on various landscapes: an empirical comparison. In: Genetic and Evolutionary Computation Conference (GECCO), pp. 479–486. ACM (2013)
Basseur, M., Goëffon, A.: Climbing combinatorial fitness landscapes. Appl. Soft Comput. 30, 688–704 (2015)
Basseur, M., Goëffon, A., Traverson, H.: Exploring non-neutral landscapes with neutrality-based local search. In: Dhaenens, C., Jourdan, L., Marmion, M.-E. (eds.) LION 2015. LNCS, vol. 8994, pp. 165–169. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19084-6_15
Bateson, W., Waunders, E.R., Punnett, R.C.: Experimental studies in the physiology of heredity. Mol. Gen. Genet. MGG 2(1), 17–19 (1909)
Gary, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York (1979)
Glover, F., Laguna, M.: Tabu Search. Springer, New York (2013). https://doi.org/10.1007/978-1-4615-6089-0
Kauffman, S.A., Weinberger, E.D.: The NK model of rugged fitness landscapes and its application to maturation of the immune response. J. Theor. Biol. 141(2), 211–245 (1989)
Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated local search. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, pp. 320–353. Springer, Boston (2003). https://doi.org/10.1007/0-306-48056-5_11
Malan, K.M., Engelbrecht, A.P.: A survey of techniques for characterising fitness landscapes and some possible ways forward. Inf. Sci. 241, 148–163 (2013)
Marmion, M.É., Jourdan, L., Dhaenens, C.: Fitness landscape analysis and metaheuristics efficiency. J. Math. Model. Algorithms 12, 3–26 (2013)
Neveu, B., Trombettoni, G., Glover, F.: ID walk: a candidate list strategy with a simple diversification device. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 423–437. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30201-8_32
Ochoa, G., Tomassini, M., Vérel, S., Darabos, C.: A study of NK landscapes’ basins and local optima networks. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 555–562. ACM (2008)
Ochoa, G., Verel, S., Tomassini, M.: First-improvement vs. best-improvement local optima networks of NK landscapes. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6238, pp. 104–113. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15844-5_11
Weinberger, E.: Correlated and uncorrelated fitness landscapes and how to tell the difference. Biol. Cybern. 63(5), 325–336 (1990)
Whitley, D., Howe, A.E., Hains, D.: Greedy or not? Best improving versus first improving stochastic local search for MAXSAT. In: AAAI (2013)
Wright, S.: The roles of mutation, inbreeding, crossbreeding, and selection in evolution, vol. 1 (1932)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Tari, S., Basseur, M., Goëffon, A. (2018). Sampled Walk and Binary Fitness Landscapes Exploration. In: Lutton, E., Legrand, P., Parrend, P., Monmarché, N., Schoenauer, M. (eds) Artificial Evolution. EA 2017. Lecture Notes in Computer Science(), vol 10764. Springer, Cham. https://doi.org/10.1007/978-3-319-78133-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-78133-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78132-7
Online ISBN: 978-3-319-78133-4
eBook Packages: Computer ScienceComputer Science (R0)