Convergence of nomadic genetic algorithm on benchmark mathematical functions

Authors:
S. Siva Sathya;M. V. Radhika
Affiliations:
Department of Computer Science, School of Engineering and Technology, Pondicherry University, Puducherry 605 014, India;Department of Computer Science, School of Engineering and Technology, Pondicherry University, Puducherry 605 014, India
Venue:
Applied Soft Computing
Year:
2013

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Abstract

Nomadic genetic algorithm is a type of multi-population migration based genetic algorithm that gives equal importance to low fit individuals and adaptively chooses its migration parameters. It has been applied to several real life applications and found to perform well compared to other genetic algorithms. This paper exploits the working of nomadic genetic algorithm (NGA) for benchmark mathematical functions and compares it with the standard genetic algorithm. To compare its performance with standard GA (SGA), the prominent mathematical functions used in optimization are used and the results proved that NGA outperforms SGA in terms of convergence speed and better optimized values.