(untitled) (International Journal of Engineering and Computer Science, 2016)
The basic idea behind this proposed method is to analyze the genetic cross over techniques using roulette wheel selection and steady state selection algorithm. The proposed algorithm is applied to find the genetic operators which are termed as mutation, crossover and selection in large dataset. The proposed technique is very useful to analyse the impact of genetic crossover techniques in random population of chromosomes. After estimating the genetic crossover technique, the efficiency of the roulette wheel and steady state selection algorithm are estimated. After estimating the efficiency of both algorithms, there is a need to compare the efficiency of roulette wheel and steady state selection algorithm based on the initial population. The extracted rules and analyzed results are graphically demonstrated. The performance is analyzed based on the different number of instances in large data set.