Spontaneous emergence of complex optimal networks through evolutionary adaptation

Abstract

An important feature of many complex systems, both natural and artificial, is the structure and organization of their interaction networks with interesting properties. Here we present a theory of self-organization by evolutionary adaptation in which we show how the structure and organization of a network is related to the survival, or in general the performance, objectives of the system. We propose that a complex system optimizes its network structure in order to maximize its overall survival fitness which is composed of short-term and long-term survival components. These in turn depend on three critical measures of the network, namely, efficiency, robustness and cost, and the environmental selection pressure. Using a graph theoretical case study, we show that when efficiency is paramount the Star topology emerges and when robustness is important the Circle topology is found. When efficiency and robustness requirements are both important to varying degrees, other classes of networks such as the Hub emerge. Our assumptions and results are consistent with observations across a wide variety of applications.