International Journal of Computational Intelligence Systems

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Volume 13, Issue 1, 2020, Pages 223 - 233

An Approach for Evolving Transformation Sequences Using Hybrid Genetic Algorithms

Authors
Ahmed Maghawry1, *, Mohamed Kholief1, Yasser Omar1, Rania Hodhod2
1Department of Computer Science, College of Computers and Information Systems, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Alexandria, Egypt
2Department of Computer Science, TSYS School of Computer Science, Columbus State University, Columbus, GA
*Corresponding author. Email: ahmed.mg.mohamed@gmail.com
Corresponding Author
Ahmed Maghawry
Received 6 May 2019, Accepted 13 February 2020, Available Online 20 February 2020.
DOI
10.2991/ijcis.d.200214.001How to use a DOI?
Keywords
Program analysis; Program transformation; Genetic algorithms; Particle swarm optimization
Abstract

The digital transformation revolution has been crawling toward almost all aspects of our lives. One form of the digital transformation revolution appears in the transformation of our routine everyday tasks into computer executable programs in the form of web, desktop and mobile applications. The vast field of software engineering that has witnessed a significant progress in the past years is responsible for this form of digital transformation. Software development as well as other branches of software engineering has been affected by this progress. Developing applications that run on top of mobile devices requires the software developer to consider the limited resources of these devices, which on one side give them their mobile advantages, however, on the other side, if an application is developed without the consideration of these limited resources then the mobile application will neither work properly nor allow the device to run smoothly. In this paper, we introduce a hybrid approach for program optimization. It succeeded in optimizing the search process for the optimal program transformation sequence that targets a specific optimization goal. In this research we targeted the program size, to reach the lowest possible decline rate of the number of Lines of Code (LoC) of a targeted program. The experimental results from applying the hybrid approach on synthetic program transformation problems show a significant improve in the optimized output on which the hybrid approach achieved an LoC decline rate of 50.51% over the application of basic genetic algorithm only where 17.34% LoC decline rate was reached.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
223 - 233
Publication Date
2020/02/20
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.200214.001How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Ahmed Maghawry
AU  - Mohamed Kholief
AU  - Yasser Omar
AU  - Rania Hodhod
PY  - 2020
DA  - 2020/02/20
TI  - An Approach for Evolving Transformation Sequences Using Hybrid Genetic Algorithms
JO  - International Journal of Computational Intelligence Systems
SP  - 223
EP  - 233
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200214.001
DO  - 10.2991/ijcis.d.200214.001
ID  - Maghawry2020
ER  -