Swarm and Evolutionary Computation
Differential evolution (DE) is one popular meta-heuristic, which is used to solve difficult optimization problems. In the last years, a huge number of new variants of the differential evolution has been introduced to outperform previously presented algorithms. To provide solutions of higher quality or to speed-up the convergence principles as control parameters adaptation, novel mutation strategies, or combination of different mutation strategies are often used. In this work, five different variants of the differential evolution have been chosen with the goal to investigate their inner dynamics, especially spread of positive genomes within the population. To capture relationships between individuals, temporal networks, more precisely contact sequences, are used. Based on the empirical results, we have concluded that temporal networks generated on the basis of the DE algorithms dynamics are non-Markovian temporal networks. For this reason, to analyze the causality-driven changes of diffusion speed in these networks, analytical methods described by Scholtes et al. have been used.
