Genetic algorithms are commonly used in many types of applications. Yet they suffer from longer execution time and premature convergence. To improve both these factors, the research work proposes two new procedural modifications in the basic genetic algorithm procedure and a new population initialization mechanism. The proposed algorithms are implemented in two types of real world problems of different sizes and the results confirm the superiority of the algorithms over existing ones both in terms of execution time and optimality.
R.Sivaraj, Novel techniques for enhancing performance of genetic algorithms, PhD thesis, Anna University, 2013.
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R.Sivaraj and T.Ravichandran, An efficient grouping genetic algorithm, International Journal of Computer Applications21(7) (2011), 38–42.
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R.Sivaraj and T.Ravichandran, An improved clustering based genetic algorithm for solving complex NP problems, Journal of Computer Science7(7) (2011), 1033–1037.
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R.Sivaraj and T.Ravichandran, A review of selection methods in genetic algorithm, International Journal of Engineering Science and Technology3(5) (2011), 3792–3797.
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R.Sivaraj and T.Ravichandran, Analyzing the impact of genetic parameters on gene grouping genetic algorithm and clustering genetic algorithm, Computer Engineering and Intelligent Systems3(1) (2012), 10–19.
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R.Sivaraj, T.Ravichandrani and R.DeviPriya, Applying gene grouping genetic algorithm for solving travelling salesman problem, International Journal of Computer Information Systems3(2) (2012), 72–76.
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R.Sivaraj, T.Ravichandrani and R.DeviPriya, Boosting performance of genetic algorithm through selective initialization, European Journal of Scientific Research68(1) (2012), 93–100.
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R.Sivaraj, T.Ravichandrani and R.DeviPriya, Solving travelling salesman problem using clustering genetic algorithm, International Journal on Computer Science and Engineering4(7) (2012), 1310–1317.
