Efficient Hill Climber for Multi-Objective Pseudo-Boolean Optimization

  • Francisco Chicano
  • Darrell Whitley
  • Renato Tinós
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9595)


Local search algorithms and iterated local search algorithms are a basic technique. Local search can be a stand-alone search method, but it can also be hybridized with evolutionary algorithms. Recently, it has been shown that it is possible to identify improving moves in Hamming neighborhoods for k-bounded pseudo-Boolean optimization problems in constant time. This means that local search does not need to enumerate neighborhoods to find improving moves. It also means that evolutionary algorithms do not need to use random mutation as a operator, except perhaps as a way to escape local optima. In this paper, we show how improving moves can be identified in constant time for multiobjective problems that are expressed as k-bounded pseudo-Boolean functions. In particular, multiobjective forms of NK Landscapes and Mk Landscapes are considered.


Hamming Ball Hill Climber Delta evaluation Multi-objective optimization Local search 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Francisco Chicano
    • 1
  • Darrell Whitley
    • 2
  • Renato Tinós
    • 3
  1. 1.Dept. de Lenguajes y Ciencias de la ComputaciónUniversity of MálagaMálagaSpain
  2. 2.Department of Computer ScienceColorado State UniversityFort CollinsUSA
  3. 3.Department of Computing and MathematicsUniversity of São PauloRibeirão PretoBrazil

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