Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Algorithm Configuration: Learning policies for the quick termination of
  poor performers   One way to speed up the algorithm configuration task is to use short runs
instead of long runs as much as possible, but without discarding the
configurations that eventually do well on the long runs. We consider the
problem of selecting the top performing configurations of the Conditional
Markov Chain Search (CMCS), a general algorithm schema that includes, for
examples, VNS. We investigate how the structure of performance on short tests
links with those on long tests, showing that significant differences arise
between test domains. We propose a "performance envelope" method to exploit the
links; that learns when runs should be terminated, but that automatically
adapts to the domain.

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The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
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The categories of this paper are: stat
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
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The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph