Performance Comparison of Roulette Wheel Selection and Steady state Selection in Genetic Nucleotide Sequence
In this paper the attempt has been made analyse the performance comparison of nucleotide sequence in DNA cells. The basic idea behind this proposed method is estimate the efficiency of roulette wheel selection and steady state selection with respect to memory and running time. Here this algorithm is applied to find the genetic operator such as mutation, crossover and selection in large dataset. In order to evaluate the proposed methodology, Comparisons are made based on the Execution time and memory efficiency in finding best fitness value in each generations and their distance estimation between each generations.. The extracted rules and analysed results are graphically demonstrated. The performance is analysed based on the different no of instances and confidence in DNA sequence data set. In genetic algorithms, the roulette wheel selection operator has spirit of utilization while steady state selection is influenced by exploration. The proposed solution is implemented in MATLAB using DNA Nucleotide Sequence of Cancer cells and the results were compared with roulette wheel selection and steady state selection with different problem sizes.