Emergent Mating Topologies in Spatially Structured Genetic Algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation, , 207-214, 2006
Abstract: The application of network analysis to emergent mating topologies in spatially structured genetic algorithms is presented in this preliminary study as a framework for inferring evolutionary dynamics in recombinant evolutionary search. Emergent mating topologies of populations evolving on regular, scale-free, and small-world imposed spatial topologies are analyzed. When the population evolves on a scale-free imposed spatial topology, the topology of mating interactions is also found to be scale-free. However, due to the random initial placement of individuals in the spatial topology, the scale-free mating topology lacks correlation between fitness and vertex connectivity, resulting in highly variable convergence rates. Scale-free mating topologies are also shown to emerge on regular imposed spatial topologies under high selection pressure. Since these scale-free emergent mating topologies self-organize such that the most-fit individuals are inherently located in highly connected vertices, such emergent mating topologies are shown to promote rapid convergence on the test problem considered herein. The emergent mating topologies of populations evolving on small-world imposed spatial topologies are not found to possess scale-free or small-world characteristics. However, due to the decrease in the characteristic path length of the emergent mating topology, the rate of population convergence is shown to increase as the imposed spatial topology is tuned from regular to small-world.
[edit database entry]
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.
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).