Displaying similar documents to “Hidden Markov random fields and the genetic structure of the scandinavian brown bear population”

The island model as a Markov dynamic system

Robert Schaefer, Aleksander Byrski, Maciej Smołka (2012)

International Journal of Applied Mathematics and Computer Science

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Parallel multi-deme genetic algorithms are especially advantageous because they allow reducing the time of computations and can perform a much broader search than single-population ones. However, their formal analysis does not seem to have been studied exhaustively enough. In this paper we propose a mathematical framework describing a wide class of island-like strategies as a stationary Markov chain. Our approach uses extensively the modeling principles introduced by Vose, Rudolph and...

Population genetics models for the statistics of DNA samples under different demographic scenarios - Maximum likelihood versus approximate methods

Andrzej Polański, Marek Kimmel (2003)

International Journal of Applied Mathematics and Computer Science

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The paper reviews the basic mathematical methodology of modeling neutral genetic evolution, including the statistics of the Fisher-Wright process, models of mutation and the coalescence method under various demographic scenarios. The basic approach is the use of maximum likelihood techniques. However, due to computational problems, intuitive or approximate methods are also of great importance.

Theoretical analysis of steady state genetic algorithms

Alexandru Agapie, Alden H. Wright (2014)

Applications of Mathematics

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Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are probabilistic algorithms for optimization, which mimic operators from natural selection and genetics. The paper analyses the convergence of the heuristic associated to a special type of Genetic Algorithm, namely the Steady State Genetic Algorithm (SSGA), considered as a discrete-time dynamical system non-generational model. Inspired by the Markov chain results in finite Evolutionary Algorithms, conditions...

Addressing the problem of lack of representativeness on syndromic surveillance schemes

Isabel Natário, M. Lucília Carvalho (2009)

Discussiones Mathematicae Probability and Statistics

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A major concern with some contagious diseases has recently led to an enormous effort to monitor population health status by several different means. This work presents a modeling approach to overcome this poor data characteristic, allowing its use for the estimation of the true population disease picture. We use a state space model, where we run two processes in parallel - a process describing the non observable states of the population concerning the presence/absence...

The impatience mechanism as a diversity maintaining and saddle crossing strategy

Iwona Karcz-Duleba (2016)

International Journal of Applied Mathematics and Computer Science

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The impatience mechanism diversifies the population and facilitates escaping from a local optima trap by modifying fitness values of poorly adapted individuals. In this paper, two versions of the impatience mechanism coupled with a phenotypic model of evolution are studied. A population subordinated to a basic version of the impatience mechanism polarizes itself and evolves as a dipole centered around an averaged individual. In the modified version, the impatience mechanism is supplied...

Linking population genetics to phylogenetics

Paul G. Higgs (2008)

Banach Center Publications

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Population geneticists study the variability of gene sequences within a species, whereas phylogeneticists compare gene sequences between species and usually have only one representative sequence per species. Stochastic models in population genetics are used to determine probability distributions for gene frequencies and to predict the probability that a new mutation will become fixed in a population. Stochastic models in phylogenetics describe the substitution process in the single sequence...

Evolutionary computation based on Bayesian classifiers

Teresa Miquélez, Endika Bengoetxea, Pedro Larrañaga (2004)

International Journal of Applied Mathematics and Computer Science

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Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). This paper contributes to the further development of this discipline...