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On Algorithm-Dependent Boundary Case Identification for Problem Classes

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7491)

Abstract

Running time analysis of metaheuristic search algorithms has attracted a lot of attention. When studying a metaheuristic algorithm over a problem class, a natural question is what are the easiest and the hardest cases of the problem class. The answer can be helpful for simplifying the analysis of an algorithm over a problem class as well as understanding the strength and weakness of an algorithm. This algorithm-dependent boundary case identification problem is investigated in this paper. We derive a general theorem for the identification, and apply it to a case that the (1+1)-EA with mutation probability less than 0.5 is used over the problem class of pseudo-Boolean functions with a unique global optimum.

Keywords

  • Markov Chain
  • Particle Swarm Optimization
  • Problem Class
  • Mutation Probability
  • Hard Case

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.

This research was supported by the National Science Foundation of China (60903103, 61105043)

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Qian, C., Yu, Y., Zhou, ZH. (2012). On Algorithm-Dependent Boundary Case Identification for Problem Classes. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32937-1_7

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  • DOI: https://doi.org/10.1007/978-3-642-32937-1_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32936-4

  • Online ISBN: 978-3-642-32937-1

  • eBook Packages: Computer ScienceComputer Science (R0)