Convergence of nomadic genetic algorithm on benchmark mathematical functions

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