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133 results on '"Hedar, Abdel-Rahman"'

8. Hybrid Genetic Algorithm for Minimum Dominating Set Problem

Author

Hedar, Abdel-Rahman, Ismail, Rashad, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Taniar, David, editor, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Pardede, Eric, editor, and Apduhan, Bernady O., editor

Published
2010
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21. Tabu Search directed by direct search methods for nonlinear global optimization

Author

Hedar, Abdel-Rahman and Fukushima, Masao

Subjects
Algorithms -- Methods, Algorithm, Business, Business, general, Business, international
Abstract

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2004.05.033 Byline: Abdel-Rahman Hedar, Masao Fukushima Keywords: Global optimization; Metaheuristics; Tabu Search; Nelder-Mead method; Adaptive pattern search Abstract: In recent years, there has been a great deal of interest in metaheuristics in the optimization community. Tabu Search (TS) represents a popular class of metaheuristics. However, compared with other metaheuristics like genetic algorithm and simulated annealing, contributions of TS that deals with continuous problems are still very limited. In this paper, we introduce a continuous TS called Directed Tabu Search (DTS) method. In the DTS method, direct-search-based strategies are used to direct a tabu search. These strategies are based on the well-known Nelder-Mead method and a new pattern search procedure called adaptive pattern search. Moreover, we introduce a new tabu list conception with anti-cycling rules called Tabu Regions and Semi-Tabu Regions. In addition, Diversification and Intensification Search schemes are employed. Numerical results show that the proposed method is promising and produces high quality solutions. Author Affiliation: Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan Article History: Received 2 July 2003; Accepted 13 May 2004 Article Note: (footnote) [star] This research was supported in part by a Grant-in-Aid for Scientific Research from the Ministry of Education, Science, Sports and Culture of Japan.

Published
2006

33. A rough set approach to feature selection based on scatter search metaheuristic

Author

M. Ibrahim Abdel-Monem, Jue Wang, Hedar Abdel-Rahman, and Qi Zhang

Subjects
business.industry, Heuristic (computer science), Pattern recognition, Feature selection, computer.software_genre, Tabu search, Reduction (complexity), Simulated annealing, Genetic algorithm, Computer Science (miscellaneous), Artificial intelligence, Rough set, Data mining, business, Metaheuristic, computer, Information Systems, Mathematics
Abstract

Rough set theory is an effective method to feature selection, which has recently fascinated many researchers. The essence of rough set approach to feature selection is to find a subset of the original features. It is, however, an NP-hard problem finding a minimal subset of the features, and it is necessary to investigate effective and efficient heuristic algorithms. This paper presents a novel rough set approach to feature selection based on scatter search metaheuristic. The proposed method, called scatter search rough set attribute reduction (SSAR), is illustrated by 13 well known datasets from UCI machine learning repository. The proposed heuristic strategy is compared with typical attribute reduction methods including genetic algorithm, ant colony, simulated annealing, and Tabu search. Computational results demonstrate that our algorithm can provide efficient solution to find a minimal subset of the features and show promising and competitive performance on the considered datasets.

Published
2014

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