Harmony Between the Best and the Worst Individuals in Tournament Selection

Abstract

Tournament selection has been widely used in Genetic Algorithms since it has many advantages such as less time complexity, O(n), applicability for parallel programming, etc. Even if Tournament selection offers more efficiency than other selection methods, it is insufficient to adjust the equilibrium between exploration and exploitation. Because of this insufficiency, it may not convergence to the optimum points closely. The paper introduces a new selection method based on standard Tournament selection. Basically, the new selection method randomly chooses individuals from the population. The method computes fitness values of contestants, then selects the best and the worst individuals between contestants in order to utilize for selection.  For some probability value, the selection method decides which individual is chosen, the best or the worst. The new technique overcomes the problem where Tournament is not sufficient by not giving up the exploration. Well-known benchmark functions are used to test the performance of the method. Moreover, the performance of new method is compared with Tournament selection to examine whether or not an improvement occurs. The results show that the new selection technique converges the optimum points closer than the standard Tournament selection.