A Blend of Markov-Chain and Drift Analysis

  • Jens Jägersküpper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


In their seminal article [Theo. Comp. Sci. 276(2002):51–82] Droste, Jansen, and Wegener present the first theoretical analysis of the expected runtime of a basic direct-search heuristic with a global search operator, namely the (1+1) Evolutionary Algorithm (EA), for the class of linear functions over the search space {0,1}n. In a rather long and involved proof they show that, for any linear function, the expected runtime of the EA is O(nlogn), i.e., that there are two constants c and n′ such that, for n ≥ n′, the expected number of iterations until a global optimum is generated is bound above by c·nlogn. However, neither c nor n′ are specified – they would be pretty large. Here we reconsider this optimization scenario to demonstrate the potential of an analytical method that makes use not only of the drift (w.r.t. a potential function, here the number of bits set correctly), but also of the distribution of the evolving candidate solution over the search space {0,1}n: An invariance property of this distribution is proved, which is then used to derive a significantly better lower bound on the drift. Finally, this better estimate of the drift results in an upper bound on the expected number of iterations of 3.8 nlog2n + 7.6log2n for n ≥ 2.


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© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jens Jägersküpper
    • 1
  1. 1.Technische Universität DortmundDortmundGermany

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