Improved aggressive and integrated aggressive selection methods for genetik algorithms

In optimization problems, Genetic Algorithms are one of the most commonly used methods to search optimum points of a given function. These algorithms stochastically select the individual that is close to the optimum point in the population. By choosing appropriate individual in each iteration, it is desired to find the best individual step by step, or converge to the best individual. Therefore, it is significantly important to have a decent selection method in genetic algorithms. In this paper, it is aimed to improve Aggressive and Integrated Aggressive Selection methods which were already proposed. The performance of the improved methods that are proposed in this paper are compared with aggressive selection methods and integrated aggressive selection methods, as well as, most commonly used standard selection methods; Roulette Wheel, Linear Ranking and Tournament. It is observed that results of improved methods are predominant comparing to the other algorithms.