Sampled Walk and Binary Fitness Landscapes Exploration

  • Sara Tari
  • Matthieu Basseur
  • Adrien Goëffon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10764)


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.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratoire d’Etude et de Recherche en Informatique d’Angers, UFR sciencesAngers Cedex 01France

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