Differential evolution is an effective population-based global optimizer which is used in many areas of research. The population consists of individuals, which are mutated, crossed and better of them survive to the next generation. In this paper, we look at this process as at the communication between individuals which can be modeled by the network where the individuals are represented by the nodes and the edges between them reflect the dynamics in the population, i.e. interactions between individuals. The main goal of this work is to find out if the differential evolution algorithm is able to create the networks where the small-world phenomenon (known as six degrees of separation) is observed. The secondary objective was to investigate the dependency between the type of the selected test function and the extent of this phenomenon. To evaluate the performance of the algorithm eleven test functions from the benchmark set CEC 2015 have been used. The analysis of the generated networks indicates that the differential evolution is able to create small-world networks in majority of test functions. As the result, the selected test functions can be classified into three categories which binds to the degree of cooperation between the individuals in the population.
['Lenka Skanderova', 'Tomas Fabian', 'Ivan Zelinka']
Small-world hidden in differential evolution