Spontaneous emergence of complex optimal networks through evolutionary adaptation
An important feature of many complex systems, both natural and artificial, is the structure and organization of their interaction networks with interesting properties. Such networks are found in a variety of applications such as in supply chain networks, computer and communication networks, metabolic networks, food webs, etc. 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- 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. Fitness maximization by adaptation leads to the spontaneous emergence of optimal network structures, both power law and non-power law, of various topologies depending on the 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. This theory provides a general conceptual framework for integrating survival or performance objectives, environmental or selection pressure, evolutionary adaptation, optimization of performance measures and topological features in a single coherent formalism. Our assumptions and results are consistent with observations across a wide variety of applications. This framework lays the ground work for a novel approach to model, design and analyze complex networks, both natural and artificial, such as metabolic pathways, supply chains and communication networks. (C) 2004 Elsevier Ltd. All rights reserved.
optimization complex networks evolution genetic algorithms