Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A deep learning guided memetic framework for graph coloring problems   Given an undirected graph $G=(V,E)$ with a set of vertices $V$ and a set of
edges $E$, a graph coloring problem involves finding a partition of the
vertices into different independent sets. In this paper we present a new
framework that combines a deep neural network with the best tools of classical
metaheuristics for graph coloring. The proposed method is evaluated on two
popular graph coloring problems (vertex coloring and weighted coloring).
Computational experiments on well-known benchmark graphs show that the proposed
approach is able to obtain highly competitive results for both problems. A
study of the contribution of deep learning in the method highlights that it is
possible to learn relevant patterns useful to obtain better solutions to graph
coloring problems.

24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat