Information dynamics in small-world boolean networks
Artificial Life
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Complex networks with multiple nodes and diverse interactions among them are ubiquitous. We suggest that optimal networks spontaneously emerge when “information” transfer is maximized at the least expense. We support our hypothesis by evolving optimal topologies for a particle swarm optimizer (PSO), a population-based stochastic algorithm. Results suggest that (1) an optimum topology emerges at the phase transition when connectivity is high enough to transfer information but low enough to prevent premature convergence, and (2) Small World (SW) networks are a compromise between higher performance and resistance to mutation. The graph characteristics of the optimal PSO networks in the SW regime are similar to that of the visual cortices of cat and macaque, thereby suggesting similar design principles. © 2006 Wiley Periodicals, Inc. Complexity 11:26–35, 2006