Statistical properties analysis of real world tournament selection in genetic algorithms

Abstract

Genetic algorithms (GAs) are probabilistic optimization methods based on the biological principle of natural evolution. One of the important operators in GAs is the selection strategy for obtaining better solutions. Specifically, finding a balance between the selection pressure and diversity is a critical issue in designing an efficient selection strategy. To this extent, the recently proposed real world tournament selection (RWTS) method has showed good performance in various benchmark problems. In this paper, we focus on analyzing characteristics of RWTS from the viewpoint of both the selection probabilities and stochastic sampling properties in order to provide a rational explanation for why RWTS provides improved performance. Statistical experimental results show that RWTS has a higher selection pressure with a relatively small loss of diversity and higher sampling accuracy than conventional tournament selection. The performance tests in a traveling salesman problem further confirm that the comparatively higher pressure and sampling accuracy, which are inherent in RWTS, can enhance the performance in the selection strategy.