A study to improve performance of genetic algorithms

Selection is one of the most crucial steps of Genetic Algorithms (GAs) that commonly used in areas of robot applications, image and voice recognition, artificial intelligence applications, path finding problems, scheduling problems, etc. In GAs, lack of adjusting the balance between exploration and exploitation, and selecting appropriate parameter settings are main problems of most selection methods as they cause premature convergence and trapping in local optima. In order to overcome these problems, two common techniques have been utilized: presenting a new selection method, tuning the parameter of an existing algorithm. In the first part of study, new selection methods, Aggressive, Non-Aggressive, Integrated Aggressive, Integrated Non-Aggressive, Outlander, Non-Aggressive Outlander, Bipolar Mating Tendency (BMT), were proposed to solve the problems. As most of the methods are based on Standard Tournament Selection (ST), their performances were compared with ST and prevalent selection methods that are also based on ST: Restricted Tournament, Unbiased Tournament, Fine-Grained Tournament and Cooperative Selections. Twenty-one well known test functions in the field of GAs were employed for the comparison. Furthermore, non-parametric statistical tests, Friedman and Wilcoxon Signed Rank, were applied to demonstrate the significance of the results. In the second part of the study, meta search methods (Brute Force and Coarse to Fine) and meta optimization algorithms (GAs, Particle Swarm Optimization and BMT) were applied to tune standard GAs in order to achieve its best performance. Moreover, the second part contains a short survey in the literature of meta optimization.