Publications
Takeover Times on Scale-Free Topologies
Proceedings of the 9th annual conference on Genetic and evolutionary computation, , 308-315, 2007
Status: Published
Citations:
Cite: [bibtex]

Abstract: The topological properties of a network directly impact the flow of information through a system. In evolving populations, the topology of inter-individual interactions affects the rate of dissemination of advantageous genetic information and thus affects selective pressure. In this study, we investigate the selective pressures induced by several scale-free population structures using takeover time analysis. Previous results have shown that the selective pressures induced by scale-free interaction topologies are at least as strong as those induced by random and panmictic interaction topologies. In contrast, our results show that the selective pressures induced by scale-free interaction topologies are heavily influenced by their underlying topological properties, and can be tuned from a selective pressure close to that of a random or panmictic topology to a selective pressure that is weaker than that of a two-dimensional toroidal lattice with 3x3 rectangular neighborhoods of interactions. We also provide a detailed topological analysis of these population structures and discuss their influence on the observed dynamics in takeover times. We show that the expected takeover times observed on all population structures considered herein can be rapidly estimated using only a few readily computable metrics of the underlying topology, precluding the need to run expensive simulations or recursive probabilistic formulations.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Continuous Self-Modeling. Science 314, 1118 (2006). [Journal Page]

Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.

Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.

Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.

Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).