Hybrid Local Search Based Genetic Algorithm and its Practical Application
Mustafa Tunay1, Rahib H. Abiyev2
1Mustafa Tunay, Department of Computer and Instructional Technologies Education, Eastern Mediterranean University, Mersin 10 Turkey, Famagusta, North Cyprus.
2Professor Dr. Rahib H. Abiyev, Department of Computer Engineering, Near East University, Mersin 10 Turkey, Nicosia, North Cyprus.
Manuscript received on April 15, 2015. | Revised Manuscript received on April 26, 2015. | Manuscript published on March 05, 2015. | PP: 21-27 | Volume-5, Issue-2, May 2015. | Retrieval Number: B2580055215/2015©BEIESP
Open Access | Ethics and Policies | Cite
©The Authors. Published By: 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: This paper presents an intense hybrid search method that uses Genetic Algorithms (GAs) and local search procedure for global optimization. The Genetic Algorithms (GAs) comprise a selection process, a crossover process and a mutation processes and local search procedure that uses Powell’s method for updating the parameters of the objective functions. The performance of the designed algorithm is tested on specific benchmarking functions namely; Rastrigin function, Rosenbrock function, Schwefel’s function 2.22, Schwefel’s function 2.21 and Sphere’s function. The computational results have demonstrated that the performance of Genetic Algorithms with Powell’s Method is much improved specific benchmarking functions. The use of a hybrid search method approach allows it to speed up the learning of the system with faster convergence rates. The Genetic Algorithm with Local Search Procedure (GALSP) is applied for soling exam timetabling problem. The GALSP seems to be a promising approach and is comparable to specialized algorithm for solving a set of global optimization problems. The algorithms of these processes have been designed and presented in the paper.
Keywords: Genetic algorithms, local search procedure, evolutionary theory, search methods..