Yıl 2018, Cilt 47, Sayı 3, Sayfalar 539 - 551 2018-06-01

Hybrid genetic algorithms for global optimization problems

Muhammad Asim [1] , Wali Mashwani Khan [2] , Özgür Yeniay [3] , Muhammad Asif Jan [4] , Nasser Tairan [5] , H. Hussian [6] , Gai-Ge Wang [7]

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In the last two decades the field evolutionary computation has become a mainstream and several types of evolutionary algorithms are developed for solving optimization and search problems. Evolutionary algorithms (EAs) are mainly inspired from the biological process of evolution. They do not demand for any concrete information such as continuity or differentiability and other information related to the problems to be solved. Due to population based nature, EAs provide a set of solutions and share properties of adaptation through an iterative process. The steepest descent methods and Broyden-Fletcher-Goldfarb-Shanno (BFGS),Hill climbing local search are quite often used for exploitation purposes in order to improve the performance of the existing EAs. In this paper, We have employed the BFGS as an additional operator in the framework of Genetic Algorithm. The idea of add-in BFGS is to sharpen the search around local optima and to speeds up the search process of the suggested algorithm. We have used 24 benchmark functions which was designed for the special session of the 2005 IEEE-Congress on Evolutionary Computation (IEEE-CEC 06) to examine the performance of the suggested hybrid GA. The experimental results provided by HGBA are much competitive and promising as compared to the stand alone GA for dealing with most of the used test problems.

In the last two decades the eld evolutionary computation has become
a mainstream and several types of evolutionary algorithms are devel-
oped for solving optimization and search problems. Evolutionary algo-
rithms (EAs) are mainly inspired from the biological process of evolu-
tion. They do not demand for any concrete information such as conti-
nuity or dierentiability and other information related to the problems
to be solved. Due to population based nature, EAs provide a set of so-
lutions and share properties of adaptation through an iterative process.
The steepest descent methods and Broyden-Fletcher-Goldfarb-Shanno
(BFGS),Hill climbing local search are quite often used for exploitation
purposes in order to improve the performance of the existing EAs. In
this paper, We have employed the BFGS as an additional operator in
the framework of Genetic Algorithm. The idea of add-in BFGS is to
sharpen the search around local optima and to speeds up the search pro-
cess of the suggested algorithm. We have used 24 benchmark functions
which was designed for the special session of the 2005 IEEE-Congress
on Evolutionary Computation (IEEE-CEC 06) to examine the perfor-
mance of the suggested hybrid GA. The experimental results provided
by HGBA are much competitive and promising as compared to the
stand alone GA for dealing with most of the used test problems.
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Birincil Dil en
Konular Matematik
Dergi Bölümü Matematik
Yazarlar

Yazar: Muhammad Asim

Yazar: Wali Mashwani Khan (Sorumlu Yazar)

Yazar: Özgür Yeniay

Yazar: Muhammad Asif Jan

Yazar: Nasser Tairan

Yazar: H. Hussian

Yazar: Gai-Ge Wang

Bibtex @araştırma makalesi { hujms439929, journal = {Hacettepe Journal of Mathematics and Statistics}, issn = {2651-477X}, eissn = {2651-477X}, address = {Hacettepe Üniversitesi}, year = {2018}, volume = {47}, pages = {539 - 551}, doi = {}, title = {Hybrid genetic algorithms for global optimization problems}, key = {cite}, author = {Yeniay, Özgür and Khan, Wali Mashwani and Tairan, Nasser and Jan, Muhammad Asif and Asim, Muhammad and Hussian, H. and Wang, Gai-Ge} }
APA Asim, M , Khan, W , Yeniay, Ö , Jan, M , Tairan, N , Hussian, H , Wang, G . (2018). Hybrid genetic algorithms for global optimization problems. Hacettepe Journal of Mathematics and Statistics, 47 (3), 539-551. Retrieved from http://dergipark.gov.tr/hujms/issue/38121/439929
MLA Asim, M , Khan, W , Yeniay, Ö , Jan, M , Tairan, N , Hussian, H , Wang, G . "Hybrid genetic algorithms for global optimization problems". Hacettepe Journal of Mathematics and Statistics 47 (2018): 539-551 <http://dergipark.gov.tr/hujms/issue/38121/439929>
Chicago Asim, M , Khan, W , Yeniay, Ö , Jan, M , Tairan, N , Hussian, H , Wang, G . "Hybrid genetic algorithms for global optimization problems". Hacettepe Journal of Mathematics and Statistics 47 (2018): 539-551
RIS TY - JOUR T1 - Hybrid genetic algorithms for global optimization problems AU - Muhammad Asim , Wali Mashwani Khan , Özgür Yeniay , Muhammad Asif Jan , Nasser Tairan , H. Hussian , Gai-Ge Wang Y1 - 2018 PY - 2018 N1 - DO - T2 - Hacettepe Journal of Mathematics and Statistics JF - Journal JO - JOR SP - 539 EP - 551 VL - 47 IS - 3 SN - 2651-477X-2651-477X M3 - UR - Y2 - 2017 ER -
EndNote %0 Hacettepe Journal of Mathematics and Statistics Hybrid genetic algorithms for global optimization problems %A Muhammad Asim , Wali Mashwani Khan , Özgür Yeniay , Muhammad Asif Jan , Nasser Tairan , H. Hussian , Gai-Ge Wang %T Hybrid genetic algorithms for global optimization problems %D 2018 %J Hacettepe Journal of Mathematics and Statistics %P 2651-477X-2651-477X %V 47 %N 3 %R %U
ISNAD Asim, Muhammad , Khan, Wali Mashwani , Yeniay, Özgür , Jan, Muhammad Asif , Tairan, Nasser , Hussian, H. , Wang, Gai-Ge . "Hybrid genetic algorithms for global optimization problems". Hacettepe Journal of Mathematics and Statistics 47 / 3 (Haziran 2018): 539-551.