Title: Performance improvement of real coded genetic algorithm with Quadratic Approximation based hybridisation
Authors: Kusum Deep, Kedar Nath Das
Addresses: Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India. ' Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
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.
Keywords: hybrid genetic algorithms; real coded genetic algorithms; Laplace crossover; power mutation; quadratic approximation; efficiency; reliability; stability.
International Journal of Intelligent Defence Support Systems, 2009 Vol.2 No.4, pp.319 - 334
Published online: 02 Feb 2010 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article