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
Evolvability Suppression to Stabilize Far-Sighted Adaptations
Artificial Life
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
An analysis of the effects of population structure on scalable multiobjective optimization problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Analysis of selection algorithms: A markov chain approach
Evolutionary Computation
The influence of scaling and assortativity on takeover times in scale-free topologies
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Effects of scale-free and small-world topologies on binary coded self-adaptive CEA
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Selection intensity in cellular evolutionary algorithms for regular lattices
IEEE Transactions on Evolutionary Computation
Graph-based evolutionary algorithms
IEEE Transactions on Evolutionary Computation
The Self-Organization of Interaction Networks for Nature-Inspired Optimization
IEEE Transactions on Evolutionary Computation
Evolutionary dynamics on scale-free interaction networks
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
In complex adaptive systems, the topological properties of the interaction network are strong governing influences on the rate of flow of information throughout the system. For example, in epidemiological models, the structure of the underlying contact network has a pronounced impact on the rate of spread of infectious disease throughout a population. Similarly, in evolutionary systems, the topology of potential mating interactions (i.e., population structure) affects the rate of flow of genetic information and therefore affects selective pressure. One commonly employed method for quantifying selective pressure in evolutionary algorithms is through the analysis of the dynamics with which a single favorable mutation spreads throughout the population (a.k.a. takeover time analysis). While models of takeover dynamics have been previously derived for several specific regular population structures, these models lack generality. In contrast, so-called pair approximations have been touted as a general technique for rapidly approximating the flow of information in spatially structured populations with a constant (or nearly constant) degree of nodal connectivities, such as in epidemiological and ecological studies. In this work, we reformulate takeover time analysis in terms of the well-known Susceptible-Infectious-Susceptible model of disease spread and adapt the pair approximation for takeover dynamics. Our results show that the pair approximation, as originally formulated, is insufficient for approximating pre-equibilibrium dynamics, since it does not properly account for the interaction between the size and shape of the local neighborhood and the population size. After parameterizing the pair approximation to account for these influences, we demonstrate that the resulting pair approximation can serve as a general and rapid approximator for takeover dynamics on a variety of spatially-explicit regular interaction topologies with varying population sizes and varying uptake and reversion probabilities. Strengths, limitations, and potential applications of the pair approximation to evolutionary computation are discussed.