Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: From feature selection to continuous optimization   Metaheuristic algorithms (MAs) have seen unprecedented growth thanks to their
successful applications in fields including engineering and health sciences. In
this work, we investigate the use of a deep learning (DL) model as an
alternative tool to do so. The proposed method, called MaNet, is motivated by
the fact that most of the DL models often need to solve massive nasty
optimization problems consisting of millions of parameters. Feature selection
is the main adopted concepts in MaNet that helps the algorithm to skip
irrelevant or partially relevant evolutionary information and uses those which
contribute most to the overall performance. The introduced model is applied on
several unimodal and multimodal continuous problems. The experiments indicate
that MaNet is able to yield competitive results compared to one of the best
hand-designed algorithms for the aforementioned problems, in terms of the
solution accuracy and scalability.

21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex