Effectiveness of scale-free properties in genetic programming

In this paper, we propose a new selection method, named scale-free selection, which is based on a scale-free network. Through study of the complex network, scale-free networks have been found in various fields. In recent years, it has been proposed that a scale-free property be applied to some optimization problems. We investigate if the new selection method is an effective selection method to apply to genetic programming. Our experimental results on three benchmark problems show that performance of the scale-free selection model is similar to the usual selection methods in spite of different optimizations and may be able to resolve the bloating problem in genetic programming. Further, we show that the optimization problem is relevant to complex network study.