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Towards a Theory of Randomized Search Heuristics

  • Ingo Wegener
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2747)

Abstract

There is a well-developed theory about the algorithmic complexity of optimization problems. Complexity theory provides negative results which typically are based on assumptions like NP≠P or NP≠RP. Positive results are obtained by the design and analysis of clever algorithms. These algorithms are well-tuned for their specific domain. Practitioners, however, prefer simple algorithms which are easy to implement and which can be used without many changes for different types of problems. They report surprisingly good results when applying randomized search heuristics like randomized local search, tabu search, simulated annealing, and evolutionary algorithms. Here a framework for a theory of randomized search heuristics is presented. It is discussed how randomized search heuristics can be delimited from other types of algorithms. This leads to the theory of black-box optimization. Lower bounds in this scenario can be proved without any complexity-theoretical assumption. Moreover, methods how to analyze randomized search heuristics, in particular, randomized local search and evolutionary algorithms are presented.

Keywords

Local Search Evolutionary Algorithm Problem Instance Search Point Randomize Search Heuristic 
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 2003

Authors and Affiliations

  • Ingo Wegener
    • 1
  1. 1.FB Informatik, LS 2Univ. DortmundDortmundGermany

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