Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Takeover time curves in random and small-world structured populations
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Emergent mating topologies in spatially structured genetic algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Takeover times on scale-free topologies
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Selection intensity in cellular evolutionary algorithms for regular lattices
IEEE Transactions on Evolutionary Computation
Graph-based evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Parameterizing pair approximations for takeover dynamics
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Effect of topology on diversity of spatially-structured evolutionary algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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The topological properties of a network directly impact the flow of information through a system. For example, in natural populations, the network of inter-individual contacts affects the rate of flow of infectious disease. Similarly, in evolutionary systems, the topological properties of the underlying population structure affect the rate of flow of genetic information, and thus affect selective pressure. One commonly employed method for quantifying the influence of the population structure on selective pressure is through the analysis of takeover time. In this study, we reformulate takeover time analysis in terms of the well-known Susceptible-Infectious-Susceptible (SIS) model of disease spread. We then adapt an analytical technique, called the pair approximation, to provide a general model of takeover dynamics. We compare the results of this model to simulation data on a total of six regular population structures and discuss the strengths and limitations of the approximation.