Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th
  Rule in Discrete Settings   While evolutionary algorithms are known to be very successful for a broad
range of applications, the algorithm designer is often left with many
algorithmic choices, for example, the size of the population, the mutation
rates, and the crossover rates of the algorithm. These parameters are known to
have a crucial influence on the optimization time, and thus need to be chosen
carefully, a task that often requires substantial efforts. Moreover, the
optimal parameters can change during the optimization process. It is therefore
of great interest to design mechanisms that dynamically choose best-possible
parameters. An example for such an update mechanism is the one-fifth success
rule for step-size adaption in evolutionary strategies. While in continuous
domains this principle is well understood also from a mathematical point of
view, no comparable theory is available for problems in discrete domains.
  In this work we show that the one-fifth success rule can be effective also in
discrete settings. We regard the $(1+(\lambda,\lambda))$~GA proposed in
[Doerr/Doerr/Ebel: From black-box complexity to designing new genetic
algorithms, TCS 2015]. We prove that if its population size is chosen according
to the one-fifth success rule then the expected optimization time on
\textsc{OneMax} is linear. This is better than what \emph{any} static
population size $\lambda$ can achieve and is asymptotically optimal also among
all adaptive parameter choices.

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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
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The categories of this paper are: stat
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The categories of this paper are: eess
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The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex