Abstract—Both exploration and exploitation are the techniques employed normally by all the optimization techniques. In genetic algorithms, the roulette wheel selection operator has essence of exploitation while rank selection is influenced by exploration. In this paper, a blend of these two selection operators is proposed that is a perfect mix of both i.e. exploration and exploitation. The blended selection operator is more exploratory in nature in initial iterations and with the passage of time, it gradually shifts towards exploitation. The proposed solution is implemented in MATLAB using travelling salesman problem and the results were compared with roulette wheel selection and rank selection with different problem sizes.
Index Terms—Genetic algorithm; rank selection; roulettewheel; selection.
Rakesh Kumar is with Department of Computer Science & Applications,
Kurukshetra University, Kurukshetra, Haryana, India (e-mail:
rsgawal@gmail.com).
Jyotishree is with Department of Computer Science, Guru Nanak Girls
College, Yamunanagar, Haryana, India (e-mail: jyotishreer@gmail.com).
Cite: Rakesh Kumar and Jyotishree, "Blending Roulette Wheel Selection & Rank Selection in Genetic Algorithms," International Journal of Machine Learning and Computing vol. 2, no. 4, pp. 365-370, 2012.