In this chapter, we do synthesis of three partially different areas of research: complex networks, evolutionary computation and deterministic chaos. Ideas, results and methodologies reported and mentioned here are based on our previous results and experiments. We report here our latest results as well as propositions on further research that is in process in our group (http://navy.cs.vsb.cz/). In order to understand what is the main idea, lets first discuss an overview of the two main areas: complex networks and evolutionary algorithms.
In this chapter, we do synthesis of three partially different areas of research: complex networks, evolutionary computation and deterministic chaos. Ideas, results and methodologies reported and mentioned here are based on our previous results and experiments. We report here our latest results as well as propositions on further research that is in process in our group (http://navy.cs.vsb.cz/). In order to understand what is the main idea, lets first discuss an overview of the two main areas: complex networks and evolutionary algorithms.
In this work, we investigate the dynamics of Differential Evolution (DE) using complex networks. In this pursuit, we would like to clarify the term complex network and analyze its properties briefly. This chapter presents a novel method for analysis of the dynamics of evolutionary algorithms in the form of complex networks. We discuss the analogy between individuals in populations in an arbitrary evolutionary algorithm and vertices of a complex network as well as between edges in a complex network and communication between individuals in a population. We also discuss the dynamics of the analysis.
Manipulating complex network structures in virtual reality and 3D printing of the results
