A Tight Runtime Analysis for the cGA on Jump Functions - EDAs Can Cross Fitness Valleys at No Extra Cost

    GECCO, 2019.

    Cited by: 11|Bibtex|Views8|Links
    EI

    Abstract:

    We prove that the compact genetic algorithm (cGA) with hypothetical population size $\mu = \Omega(\sqrt n \log n) \cap \text{poly}(n)$ with high probability finds the optimum of any $n$-dimensional jump function with jump size $k < \frac 1 {20} \ln n$ in $O(\mu \sqrt n)$ iterations. Since it is known that the cGA with high probability n...More

    Code:

    Data:

    Your rating :
    0

     

    Best Paper
    Best Paper of GECCO, 2019
    Tags
    Comments