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An improved genetic algorithm for solving optimization problems / Mraga Mohamed Atiat Allah ; Supervised Hesham Ahmed Hefny

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mraga Mohamed Atiat Allah , 2021Description: 84 Leaves : charts , facsimiles ; 30cmOther title:
  • خوارزم جينى محسن لحل مشكلات الامثلية [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Computer and Information Science Summary: Genetic algorithm (GA) is a branch of the so-called evolutionary computing (EC) that is inspired by the evolution of living beings in nature. GA is considered a powerful tool for solving many optimization problems.The searching ability of GA depends on different parameters such as: population size, number of generations, crossover operator and mutation operator.The proper selection of such parameters received much attention of the researchers in order to achieve fast convergence to near optimal solution and to avoid stucking into local minima.In this thesis, we propose a new method for improving the search mechanism of GA.The proposed method depends on adopting different conditional crossover operators based on Local Partitioning of the population during the selection process.The proposed method is evaluated in the experimental work through twelve benchmark optimization problems and compared with conventional real-valued coded GA.The results ensure the efficacy and competitivity of the proposed method
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.02.M.Sc.2021.Mr.I (Browse shelf(Opens below)) Not for loan 01010110084965000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.02.M.Sc.2021.Mr.I (Browse shelf(Opens below)) 84965.CD Not for loan 01020110084965000

Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Computer and Information Science

Genetic algorithm (GA) is a branch of the so-called evolutionary computing (EC) that is inspired by the evolution of living beings in nature. GA is considered a powerful tool for solving many optimization problems.The searching ability of GA depends on different parameters such as: population size, number of generations, crossover operator and mutation operator.The proper selection of such parameters received much attention of the researchers in order to achieve fast convergence to near optimal solution and to avoid stucking into local minima.In this thesis, we propose a new method for improving the search mechanism of GA.The proposed method depends on adopting different conditional crossover operators based on Local Partitioning of the population during the selection process.The proposed method is evaluated in the experimental work through twelve benchmark optimization problems and compared with conventional real-valued coded GA.The results ensure the efficacy and competitivity of the proposed method

Issued also as CD

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