Publications

@inproceedings{10.1145/2816839.2816843,
title = {Deterministically-Adaptive Genetic Algorithm To Solve Binary Communication Problems: Application On The Error Correcting Code Problem},
author = {Zakaria Abdelmoiz Dahi and Chaker, Mezioud and Draa, Amer},
url = {https://doi.org/10.1145/2816839.2816843},
doi = {10.1145/2816839.2816843},
isbn = {9781450334587},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication},
publisher = {Association for Computing Machinery},
address = {Batna, Algeria},
series = {IPAC '15},
abstract = {Global optimisation plays a critical role in today's scientific and industrial fields.
Optimisation problems are either continuous or combinatorial depending on the nature
of the parameters to optimise. In the class of combinatorial problems, we find a sub-category
which is the binary optimisation problems. Due to the complex nature of optimisation
problems, exhaustive search-based methods are no longer a good choice. So, metaheuristics
are more and more being opted in order to solve such problems. On the other hand,
most of the proposed metaheuristics were hand-tuned through a long and exhaustive
process that requires advanced knowledge. This fact makes them sensitive to any change
of the problem properties, that probably might decrease their efficiency. So, their
further application in real-life scenarios will be restricted or impossible. One of
the most active topic of research of nowdays is the adaptation strategies. These last
ones appear as a promising alternative to the hand-tuned approach. Deterministic adaptation
is one of the several adaptation schemes that exist. Based on the latter, in this
paper we propose several variants of one of the most studied metaheuristics; the Genetic
Algorithm (GA). The efficiency of the variants was assessed for solving a complex
optimisation problem in cellular networks which is the Error Correcting Code Problem
(ECCP). They were compared against a classical hand-tuned genetic algorithm. The experiments
gave promising results and encourage further investigation.},
keywords = {Adaptation Strategies, Cellular Phone Networks, Error Correcting Code Problem, evolutionary algorithms, Genetic Algorithm, Transmission},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Ferrer2009b,
title = {On the correlation between static measures and code coverage using evolutionary test case generation},
author = {Javier Ferrer and Francisco Chicano and Enrique Alba},
year = {2009},
date = {2009-01-01},
booktitle = {ADIS 2009 - Apoyo a la Decision en Ingenieria del Software, Evento Realizado en el Marco de las 14th Jornadas de Ingenieria del Software y Bases de Datos, JISBD 2009},
volume = {3},
number = {1},
abstract = {Evolutionary testing is a very popular domain in the field of search based software engineering that consists in automatically generating test cases for a given piece of code using evolutionary algorithms. One of the most important measures used to evaluate the quality of the generated test suites is code coverage. In this paper we want to analyze if there exists a correlation between some static measures computed on the test program and the code coverage when an evolutionary test case generator is used. In particular, we use Evolutionary Strategies (ES) as search engine of the test case generator. We have also developed a program generator that is able to create Java programs with the desired values of the static measures. The experimental study includes a benchmark of 3600 programs automatically generated to find correlations between the measures. The results of this study can be used in future work for the development of a tool that decides the test case generation method according to the static measures computed on a given program.},
keywords = {branch coverage, Evo- lutionary strategy, evolutionary algorithms, evolutionary testing},
pubstate = {published},
tppubtype = {inproceedings}
}