Deterministic Divide-and-Conquer Algorithm for Decomposable Black-Box Optimization Problems with Bounded Difficulty

  • Shude Zhou
  • Zengqi Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


There is a class of GA-hard problems for which classical genetic algorithms often fail to obtain optimal solutions. In this paper, we focus on a class of very typical GA-hard problems that we call decomposable black-box optimization problems (DBBOP). Different from random methods in GA literature, two “deterministic” divide-and-conquer algorithms DA1 and DA2 are proposed respectively for non-overlapping and overlapping DBBOP, in which there are no classical genetic operations and even no random operations. Given any DBBOP with dimension l and bounded order k, our algorithms can always reliably and accurately obtain the optimal solutions in deterministic way using O(l k ) function evaluations.


Genetic Algorithm Time Complexity Evolutionary Computation Decomposition Algorithm Deterministic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shude Zhou
    • 1
  • Zengqi Sun
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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