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main.tex
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\documentclass[sigconf]{acmart}
\usepackage{booktabs} % For formal tables
\usepackage{mathtools}
\usepackage{setspace}
\usepackage{subcaption}
% For subcaption
\captionsetup[subfigure]{labelformat=simple}
\renewcommand{\thesubfigure}{(\alph{subfigure})}
% Copyright
%\setcopyright{none}
%\setcopyright{acmcopyright}
%\setcopyright{acmlicensed}
\setcopyright{rightsretained}
%\setcopyright{usgov}
%\setcopyright{usgovmixed}
%\setcopyright{cagov}
%\setcopyright{cagovmixed}
% DOI
\acmDOI{10.1145/3319619.3326774}
% ISBN
\acmISBN{978-1-4503-6748-6/19/07}
% Conference
\acmConference[GECCO '19]{the Genetic and Evolutionary Computation Conference 2019}{July 13--17, 2019}{Prague, Czech Republic}
\acmYear{2019}
\copyrightyear{2019}
%\acmArticle{4}
\acmPrice{15.00}
% These commands are optional
\acmBooktitle{Genetic and Evolutionary Computation Conference Companion (GECCO '19 Companion), July 13--17, 2019, Prague, Czech Republic}
%\editor{Jennifer B. Sartor}
%\editor{Theo D'Hondt}
%\editor{Wolfgang De Meuter}
\begin{document}
\title{Stabilized Training of Generative Adversarial Networks by a Genetic Algorithm}
%% Authors
\author{Hwi-Yeon Cho}
\orcid{0000-0002-4604-2578}
\affiliation{%
\institution{Department of Computer Science, Kwangwoon University}
\city{Seoul}
\country{Republic of Korea}
}
\email{hwyn.cho@gmail.com}
\author{Yong-Hyuk Kim}
\orcid{0000-0002-0492-0889}
\affiliation{%
\institution{Department of Computer Science, Kwangwoon University}
\city{Seoul}
\state{Republic of Korea}
}
\email{yhdfly@kw.ac.kr}
% The default list of authors is too long for headers.
\renewcommand{\shortauthors}{H.-Y. Cho et al.}
\begin{abstract}
Generative adversarial networks (GAN) facilitate the learning of probability distributions of complex data in the real world, and allow neural networks to generate the distribution. GANs (GAN and its variants) exhibit excellent performance in applications like image generation and video generation. However, GANs sometimes experience problems during training with regard to the distribution of real data. We applied a genetic algorithm to improve and optimize the GAN’s training performance. As a result, the convergence speed and stability during the training process improved compared to the conventional GAN.
\end{abstract}
%
% The code below should be generated by the tool at http://dl.acm.org/ccs.cfm
% Please copy and paste the code instead of the example below.
%
\begin{CCSXML}
<ccs2012>
<concept>
<concept_id>10010147.10010257.10010293.10011809.10011812</concept_id>
<concept_desc>Computing methodologies~Genetic algorithms</concept_desc>
<concept_significance>500</concept_significance>
</concept>
<concept>
<concept_id>10010147.10010257.10010293.10010294</concept_id>
<concept_desc>Computing methodologies~Neural networks</concept_desc>
<concept_significance>300</concept_significance>
</concept>
</ccs2012>
\end{CCSXML}
\ccsdesc[500]{Computing methodologies~Genetic algorithms}
\ccsdesc[300]{Computing methodologies~Neural networks}
\keywords{genetic algorithm, neural networks}
\maketitle
\input{body}
\begin{acks}
\footnotesize
This research was supported by a grant [KCG-01-2017-05] through the Disaster and Safety Management Institute funded by Korea Coast Guard of Korean government.
\par
\end{acks}
\bibliographystyle{ieeetr}
\bibliography{bibliography}
\end{document}