The Study Of Genetic Information Flux Network Properties In Genetic Algorithms

Since Watts and Strigatz’s work in small‐world network in1998and Barabàsi and Albert’s work in scale‐free in1999, an explodion of work about complex networks emerges, from the analysis of the topology of real networks, the evolution model and dynamics of complex networks to the application of the complex network theory. The genetic algorithm (GA) is a heuristic search algorithm that mimics the process of natural evolution, and in itself is a complex dynamical evolving network. With the development of complex network theory, people start studying the GA based on it. As the population structure of GA directly influences the combination and distribution of excellent gene segments of the population, many studies have focused on the GA’s population structure. The studied population structures mainly include random network, small‐world network and scale‐free network, and a series of relationship between the topological properties of a population structure and the performance of the algorithm (for example takeover time) it induces on a population has built.The IFN that describes the actual interaction topology between individuals of GA offers us a new viewpoint to study GA. Through the study of IFN we will better understand the characteristic of GA at different operation conditions. Now that inducing the change of scaling exponent of the power‐law node degree distribution of IFN is too much complicated, this paper is aimed at the IFN characteristic of GA at different operator and giving different interpretations from previous researches done by others.An empirical analysis is done on the information flux network (IFN) statistical properties of genetic algorithms (GA) and the results suggest that the node degree distribution of IFN is scale‐free when there is at least some selection pressure, and it has two branches as node degree is small. Increasing crossover, decreasing the mutation rate or decreasing the selective pressure will increase the average node degree, thus leading to the decrease of scaling exponent. These studies will be helpful in understanding the combination and distribution of excellent gene segments of the population in GA evolving, and will be useful in devising an efficient GA.