Monogamous Pairs Genetic Algorithm (Mop GA)
Ting Yee Lim1, Ahamad Tajudin Khader2
1Lim Ting Yee, School of Computer Science, Universiti Sains Malaysia (USM), Penang, Malaysia.
2Ahamad Tajudin Khader, School of Computer Science, Universiti Sains Malaysia (USM), Penang, Malaysia.
Manuscript received on May 16, 2013. | Revised Manuscript received on June 09, 2013. | Manuscript published on June 30, 2013. | PP: 143-149 | Volume-2, Issue-5, June 2013. | Retrieval Number: E1718062513/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: As the race in producing better Genetic Algorithms (GAs) to alleviate the notorious premature convergence problems heats on, the danger of overly complex solutions, ignoring the practicality and feasibility of basic algorithms continues in some researches. In this paper, we propose a new variant of GA with decent complexity without loosing the search power. Our approach is inspired by the monogamous behavior observed in nature. The efficacy of MopGA is verified on nine benchmark numerical test functions. The results are mostly comparable to standard GA and even achieve better overall average reliability and speed.
Keywords: Genetic algorithm, monogamy, numerical function optimization.