Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus
  Reinforcement Learning   The immensity of the string landscape and the difficulty of identifying
solutions that match the observed features of particle physics have raised
serious questions about the predictive power of string theory. Modern methods
of optimisation and search can, however, significantly improve the prospects of
constructing the standard model in string theory. In this paper we scrutinise a
corner of the heterotic string landscape consisting of compactifications on
Calabi-Yau three-folds with monad bundles and show that genetic algorithms can
be successfully used to generate anomaly-free supersymmetric SO(10) GUTs with
three families of fermions that have the right ingredients to accommodate the
standard model. We compare this method with reinforcement learning and find
that the two methods have similar efficacy but somewhat complementary
characteristics.

23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex