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Performance improvement of real coded genetic algorithm with Quadratic Approximation based hybridisation
by Kusum Deep, Kedar Nath Das
International Journal of Intelligent Defence Support Systems (IJIDSS), Vol. 2, No. 4, 2009

 

Abstract: Due to their diversity preserving mechanism, real coded genetic algorithms are extremely popular in solving complex non-linear optimisation problems. In recent literature, Deep and Thakur (2007a, 2007b) proved that the new real coded genetic algorithm (called LX-PM that uses Laplace Crossover and Power Mutation) is more efficient than the existing genetic algorithms that use combinations of Heuristic Crossover along with Non-Uniform or Makinen, Periaux and Toivanen Mutation. However, there are some instances where LX-PM needs improvement. Hence, in this paper, an attempt is made to improve the efficiency and reliability of this existing LX-PM by hybridising it with quadratic approximation (called H-LX-PM). To realise the improvement, a set of 22 benchmark test problems and two real world problems, namely: a) system of linear equations; b) frequency modulation parameter identification problem, have been considered. The numerical and graphical results confirm that H-LX-PM really exhibits improvement over LX-PM in terms of efficiency, reliability and stability.

Online publication date: Tue, 02-Feb-2010

 

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