The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Riccardo Poli - One of the best experts on this subject based on the ideXlab platform.
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Mean and Variance of the Sampling Distribution of Particle Swarm Optimizers During Stagnation
IEEE Transactions on Evolutionary Computation, 2009Co-Authors: Riccardo PoliAbstract:Several theoretical analyses of the dynamics of particle swarms have been offered in the literature over the last decade. Virtually all rely on substantial simplifications, often including the assumption that the particles are deterministic. This has prevented the exact characterization of the Sampling Distribution of the particle swarm optimizer (PSO). In this paper we introduce a novel method that allows us to exactly determine all the characteristics of a PSO Sampling Distribution and explain how it changes over any number of generations, in the presence stochasticity. The only assumption we make is stagnation, i.e., we study the Sampling Distribution produced by particles in search for a better personal best. We apply the analysis to the PSO with inertia weight, but the analysis is also valid for the PSO with constriction and other forms of PSO.
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dynamics and stability of the Sampling Distribution of particle swarm optimisers via moment analysis
Journal of Artificial Evolution and Applications, 2008Co-Authors: Riccardo PoliAbstract:For stochastic optimisation algorithms, knowing the probability Distribution with which an algorithm allocates new samples in the search space is very important, since this explains how the algorithm really works and is a prerequisite to being able to match algorithms to problems. This is the only way to beat the limitations highlighted by the no-free lunch theory. Yet, the Sampling Distribution for velocity-based particle swarm optimisers has remained a mystery for the whole of the first decade of PSO research. In this paper, a method is presented that allows one to exactly determine all the characteristics of a PSO's Sampling Distribution and explain how it changes over time during stagnation (i.e., while particles are in search for a better personal best) for a large class of PSO's.
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on the moments of the Sampling Distribution of particle swarm optimisers
Genetic and Evolutionary Computation Conference, 2007Co-Authors: Riccardo PoliAbstract:A method is presented that allows one to exactly determine all the characteristics of a PSO's Sampling Distribution and explain how it changes over time, in the presence stochasticity. The only assumption made is stagnation (particles are in search for a better personal best).
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exact analysis of the Sampling Distribution for the canonical particle swarm optimiser and its convergence during stagnation
Genetic and Evolutionary Computation Conference, 2007Co-Authors: Riccardo Poli, D S BroomheadAbstract:Several theoretical analyses of the dynamics of particle swarms have been offered in the literature over the last decade. Virtually all rely on substantial simplifications, including the assumption that the particles are deterministic. This has prevented the exact characterisation of the Sampling Distribution of the PSO. In this paper we introduce a novel method, which allows one to exactly determine all the characteristics of a PSO's Sampling Distribution and explain how they change over any number of generations, in the presence stochasticity. The only assumption we make is stagnation, i.e., we study the Sampling Distribution produced by particles in search for a better personal best. We apply the analysis to the PSO with inertia weight, but the analysis is also valid for the PSO with constriction.
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CSM-465: The Sampling Distribution of Particle Swarm Optimisers and their Stability
2007Co-Authors: Riccardo PoliAbstract:Several theoretical analyses of the dynamics of particle swarms have been offered in the literature over the last decade. Virtually all rely on substantial simplifications, often including the assumption that the particles are deterministic. This has prevented the exact characterisation of the Sampling Distribution of the PSO. In this paper we introduce a novel method that allows us to exactly determine all the characteristics of a PSO's Sampling Distribution and explain how it changes over any number of generations, in the presence stochasticity. The only assumption we make is stagnation, i.e., we study the Sampling Distribution produced by particles in search for a better personal best. We apply the analysis to the PSO with inertia weight, but the analysis is also valid for the PSO with constriction and other forms of PSO.
Yun S Song - One of the best experts on this subject based on the ideXlab platform.
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a sequentially markov conditional Sampling Distribution for structured populations with migration and recombination
Theoretical Population Biology, 2013Co-Authors: Matthias Steinrucken, Joshua S Paul, Yun S SongAbstract:Conditional Sampling Distributions (CSDs), sometimes referred to as copying models, underlie numerous practical tools in population genomic analyses. Though an important application that has received much attention is the inference of population structure, the explicit exchange of migrants at specified rates has not hitherto been incorporated into the CSD in a principled framework. Recently, in the case of a single panmictic population, a sequentially Markov CSD has been developed as an accurate, efficient approximation to a principled CSD derived from the diffusion process dual to the coalescent with recombination. In this paper, the sequentially Markov CSD framework is extended to incorporate subdivided population structure, thus providing an efficiently computable CSD that admits a genealogical interpretation related to the structured coalescent with migration and recombination. As a concrete application, it is demonstrated empirically that the CSD developed here can be employed to yield accurate estimation of a wide range of migration rates.
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estimating variable effective population sizes from multiple genomes a sequentially markov conditional Sampling Distribution approach
Genetics, 2013Co-Authors: Sara Sheehan, Kelley Harris, Yun S SongAbstract:Throughout history, the population size of modern humans has varied considerably due to changes in environment, culture, and technology. More accurate estimates of population size changes, and when they occurred, should provide a clearer picture of human colonization history and help remove confounding effects from natural selection inference. Demography influences the pattern of genetic variation in a population, and thus genomic data of multiple individuals sampled from one or more present-day populations contain valuable information about the past demographic history. Recently, Li and Durbin developed a coalescent-based hidden Markov model, called the pairwise sequentially Markovian coalescent (PSMC), for a pair of chromosomes (or one diploid individual) to estimate past population sizes. This is an efficient, useful approach, but its accuracy in the very recent past is hampered by the fact that, because of the small sample size, only few coalescence events occur in that period. Multiple genomes from the same population contain more information about the recent past, but are also more computationally challenging to study jointly in a coalescent framework. Here, we present a new coalescent-based method that can efficiently infer population size changes from multiple genomes, providing access to a new store of information about the recent past. Our work generalizes the recently developed sequentially Markov conditional Sampling Distribution framework, which provides an accurate approximation of the probability of observing a newly sampled haplotype given a set of previously sampled haplotypes. Simulation results demonstrate that we can accurately reconstruct the true population histories, with a significant improvement over the PSMC in the recent past. We apply our method, called diCal, to the genomes of multiple human individuals of European and African ancestry to obtain a detailed population size change history during recent times.
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Padé approximants and exact two-locus Sampling Distributions
Annals of Applied Probability, 2012Co-Authors: Paul A. Jenkins, Yun S SongAbstract:For population genetics models with recombination, obtaining an exact, analytic Sampling Distribution has remained a challenging open problem for several decades. Recently, a new perspective based on asymptotic series has been introduced to make progress on this problem. Specifically, closed-form expressions have been derived for the first few terms in an asymptotic expansion of the two-locus Sampling Distribution when the recombination rate ρ is moderate to large. In this paper, a new computational technique is developed for finding the asymptotic expansion to an arbitrary order. Computation in this new approach can be automated easily. Furthermore, it is proved here that only a finite number of terms in the asymptotic expansion is needed to recover (via the method of Pade approximants) the exact two-locus Sampling Distribution as an analytic function of ρ; this function is exact for all values of ρ ∈ [0, ∞). It is also shown that the new computational framework presented here is flexible enough to incorporate natural selection.
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an accurate sequentially markov conditional Sampling Distribution for the coalescent with recombination
Genetics, 2011Co-Authors: Joshua S Paul, Matthias Steinrucken, Yun S SongAbstract:The sequentially Markov coalescent is a simplified genealogical process that aims to capture the essential features of the full coalescent model with recombination, while being scalable in the number of loci. In this article, the sequentially Markov framework is applied to the conditional Sampling Distribution (CSD), which is at the core of many statistical tools for population genetic analyses. Briefly, the CSD describes the probability that an additionally sampled DNA sequence is of a certain type, given that a collection of sequences has already been observed. A hidden Markov model (HMM) formulation of the sequentially Markov CSD is developed here, yielding an algorithm with time complexity linear in both the number of loci and the number of haplotypes. This work provides a highly accurate, practical approximation to a recently introduced CSD derived from the diffusion process associated with the coalescent with recombination. It is empirically demonstrated that the improvement in accuracy of the new CSD over previously proposed HMM-based CSDs increases substantially with the number of loci. The framework presented here can be adopted in a wide range of applications in population genetics, including imputing missing sequence data, estimating recombination rates, and inferring human colonization history.
Jeandominique Creutin - One of the best experts on this subject based on the ideXlab platform.
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analytical solutions to Sampling effects in drop size Distribution measurements during stationary rainfall estimation of bulk rainfall variables
Journal of Hydrology, 2006Co-Authors: R Uijlenhoet, Josep M Porra, Daniel Sempere Torres, Jeandominique CreutinAbstract:A stochastic model of the microstructure of rainfall is used to derive explicit expressions for the magnitude of the Sampling fluctuations in rainfall properties estimated from raindrop size measurements in stationary rainfall. The model is a marked point process, in which the points represent the drop centers, assumed to be uniformly distributed in space. This assumption, which is supported both by theoretical and by empirical evidence, implies that during periods of stationary rainfall the number of drops in a sample volume follows a Poisson Distribution. The marks represent the drop sizes, assumed to be distributed independent of their positions according to some general drop size Distribution. Within this framework, it is shown analytically how the Sampling Distribution of the estimator of any bulk rainfall variable (such as liquid water content, rain rate, or radar reflectivity) in stationary rainfall converges from a strongly skewed Distribution to a (symmetrical) Gaussian Distribution with increasing sample size. The relevant parameter controlling this evolution is the average number of drops in the sample ns. For a given sample size, the skewness of the Sampling Distribution is found to be more pronounced for higher order moments of the drop size Distribution. For instance, the Sampling Distribution of the normalized mean diameter becomes nearly Gaussian for ns > 10, while the Sampling Distribution of the normalized rain rate remains skewed for ns 500. Additionally, it is shown analytically that, as a result of the mentioned skewness, the median Q50 as an estimator of a bulk rainfall variable always underestimates its population value Qp in stationary rainfall. The ratio of the former to the latter is found to be , where b is a constant depending on the drop size Distribution. For bulk rainfall variables this constant is positive and therefore the median always underestimates the population value. This provides a theoretical confirmation and explanation of previously published simulation results. Finally, relationships between the expected number of raindrops in the sample ns and the rain rate are established for different parametric forms of the raindrop size Distribution. These relationships are first compared to experimental results and then used to provide examples of Sampling Distributions of bulk rainfall variables (in this case rain rate) for different values of the average rain rate and different integration times of the disdrometric device involved (in this case a Joss?Waldvogel disdrometer). The practical relevance of these results is (1) that they provide exact solutions to the Sampling problem during (relatively rare) periods of stationary rainfall (e.g., drizzle), and (2) that they provide a lower bound to the magnitude of the Sampling problem in the general situation where Sampling fluctuations and natural variability co-exist.
D S Broomhead - One of the best experts on this subject based on the ideXlab platform.
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exact analysis of the Sampling Distribution for the canonical particle swarm optimiser and its convergence during stagnation
Genetic and Evolutionary Computation Conference, 2007Co-Authors: Riccardo Poli, D S BroomheadAbstract:Several theoretical analyses of the dynamics of particle swarms have been offered in the literature over the last decade. Virtually all rely on substantial simplifications, including the assumption that the particles are deterministic. This has prevented the exact characterisation of the Sampling Distribution of the PSO. In this paper we introduce a novel method, which allows one to exactly determine all the characteristics of a PSO's Sampling Distribution and explain how they change over any number of generations, in the presence stochasticity. The only assumption we make is stagnation, i.e., we study the Sampling Distribution produced by particles in search for a better personal best. We apply the analysis to the PSO with inertia weight, but the analysis is also valid for the PSO with constriction.
Joshua S Paul - One of the best experts on this subject based on the ideXlab platform.
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a sequentially markov conditional Sampling Distribution for structured populations with migration and recombination
Theoretical Population Biology, 2013Co-Authors: Matthias Steinrucken, Joshua S Paul, Yun S SongAbstract:Conditional Sampling Distributions (CSDs), sometimes referred to as copying models, underlie numerous practical tools in population genomic analyses. Though an important application that has received much attention is the inference of population structure, the explicit exchange of migrants at specified rates has not hitherto been incorporated into the CSD in a principled framework. Recently, in the case of a single panmictic population, a sequentially Markov CSD has been developed as an accurate, efficient approximation to a principled CSD derived from the diffusion process dual to the coalescent with recombination. In this paper, the sequentially Markov CSD framework is extended to incorporate subdivided population structure, thus providing an efficiently computable CSD that admits a genealogical interpretation related to the structured coalescent with migration and recombination. As a concrete application, it is demonstrated empirically that the CSD developed here can be employed to yield accurate estimation of a wide range of migration rates.
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an accurate sequentially markov conditional Sampling Distribution for the coalescent with recombination
Genetics, 2011Co-Authors: Joshua S Paul, Matthias Steinrucken, Yun S SongAbstract:The sequentially Markov coalescent is a simplified genealogical process that aims to capture the essential features of the full coalescent model with recombination, while being scalable in the number of loci. In this article, the sequentially Markov framework is applied to the conditional Sampling Distribution (CSD), which is at the core of many statistical tools for population genetic analyses. Briefly, the CSD describes the probability that an additionally sampled DNA sequence is of a certain type, given that a collection of sequences has already been observed. A hidden Markov model (HMM) formulation of the sequentially Markov CSD is developed here, yielding an algorithm with time complexity linear in both the number of loci and the number of haplotypes. This work provides a highly accurate, practical approximation to a recently introduced CSD derived from the diffusion process associated with the coalescent with recombination. It is empirically demonstrated that the improvement in accuracy of the new CSD over previously proposed HMM-based CSDs increases substantially with the number of loci. The framework presented here can be adopted in a wide range of applications in population genetics, including imputing missing sequence data, estimating recombination rates, and inferring human colonization history.
