Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative Adversarial Networks in Estimation of Distribution Algorithms
  for Combinatorial Optimization   Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Generative Adversarial
Networks (GAN) are generative neural networks which can be trained to
implicitly model the probability distribution of given data, and it is possible
to sample this distribution. We integrate a GAN into an EDA and evaluate the
performance of this system when solving combinatorial optimization problems
with a single objective. We use several standard benchmark problems and compare
the results to state-of-the-art multivariate EDAs. GAN-EDA doe not yield
competitive results - the GAN lacks the ability to quickly learn a good
approximation of the probability distribution. A key reason seems to be the
large amount of noise present in the first EDA generations.

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The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat