Biology has given us several new computing paradigms, and it has also provided us with many elegant solutions to difficult computing and communications problems. One such field is evolutionary computing, which is based on the principles given by Charles Darwin. In particular, evolutionary algorithms (EAs) have been used to find optimal solutions to many optimization problems. This has given us many diverse interdisciplinary applications of EAs in engineering and science. Complex systems are one such application. Complex systems are interactive selfish agents, investing in relations with each other for a personal gain. Each has access to limited resources such as money, time, and energy.

In this paper, the author discusses the evolution of such complex systems as networks where nodes represent agents and the edges between them represent relations. She illustrates her point with two examples of social networks and power-law networks. Finally, in a formal framework, she provides evolutionary principles for the evaluation of complex systems of networks. In this framework, she illustrates the procedure for a connected tree--that is, a graph without cycles--and shows that this yields networks with stable properties in reasonable time.