Incremental & classical genetic algorithm. (c2001)

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dc.contributor.author Awad, Mohamad M.
dc.date.accessioned 2011-10-13T09:34:44Z
dc.date.available 2011-10-13T09:34:44Z
dc.date.copyright 2001 en_US
dc.date.issued 2011-10-13
dc.date.submitted 2001-08-06
dc.identifier.uri http://hdl.handle.net/10725/759
dc.description Includes bibliographical references (p. 41-43). en_US
dc.description.abstract A Classical Genetic Algorithm (CGA) is known to find an optimal or near optimal solution for complex and difficult problems. However, there are many cases where these problems are subject to frequent modifications each producing a new problem, if these new problems are large, it is costly to use a genetic algorithm to reoptimize these problems after each modification. In this thesis, we propose an Incremental Genetic Algorithm (IGA) to reduce the time needed to reoptimize large-scale modified problems. To validate the proposed approach, we consider three problems: optimal regression testing, general optimization, and exam scheduling. In addition, we develop a hybrid genetic algorithm (HGA) for the problem in order to improve the results of a classical genetic algorithm. The experimental results obtained by applying IGA to the three optimization problems, show that IGA requires a smaller number of generations and less time than that of CGA to converge to a solution. In addition, the quality of the solutions produced by IGA is similar or slightly better than that of the CGA. en_US
dc.language.iso en en_US
dc.subject Genetic algorithms en_US
dc.title Incremental & classical genetic algorithm. (c2001) en_US
dc.type Thesis en_US
dc.term.submitted Summer II en_US
dc.author.degree MS in Computer Science en_US
dc.author.school Arts and Sciences en_US
dc.author.idnumber 198504190 en_US
dc.author.commembers Dr. Ramzi Haraty
dc.author.commembers Dr. Khaled El Fakih
dc.author.woa RA en_US
dc.description.physdesc 1 bound copy: v, 43 leaves; ill., tables; 30 cm. available at RNL. en_US
dc.author.division Computer Science en_US
dc.author.advisor Dr. Nashat Mansour
dc.keywords Artificial intelligence en_US
dc.keywords Incremental genetic algorithms en_US
dc.keywords Application of genetic algorithm en_US
dc.keywords Optimization algorithm en_US
dc.keywords Exam scheduling en_US
dc.keywords General optimization en_US
dc.keywords Regression testing en_US
dc.identifier.doi https://doi.org/10.26756/th.2001.14 en_US
dc.publisher.institution Lebanese American University en_US

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