20,716 research outputs found
Efficiency Analysis of Swarm Intelligence and Randomization Techniques
Swarm intelligence has becoming a powerful technique in solving design and
scheduling tasks. Metaheuristic algorithms are an integrated part of this
paradigm, and particle swarm optimization is often viewed as an important
landmark. The outstanding performance and efficiency of swarm-based algorithms
inspired many new developments, though mathematical understanding of
metaheuristics remains partly a mystery. In contrast to the classic
deterministic algorithms, metaheuristics such as PSO always use some form of
randomness, and such randomization now employs various techniques. This paper
intends to review and analyze some of the convergence and efficiency associated
with metaheuristics such as firefly algorithm, random walks, and L\'evy
flights. We will discuss how these techniques are used and their implications
for further research.Comment: 10 pages. arXiv admin note: substantial text overlap with
arXiv:1212.0220, arXiv:1208.0527, arXiv:1003.146
Cooperation of Nature and Physiologically Inspired Mechanism in Visualisation
A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised. The performance of the nature-inspired algorithms and the biologically inspired mechanisms in the hybrid algorithm is reflected through a cooperative attempt to make a drawing on the canvas. The scientific value of the marriage between the two swarm intelligence algorithms is currently being investigated thoroughly on many benchmarks and the results reported suggest a promising prospect (al-Rifaie, Bishop & Blackwell, 2011). We also discuss whether or not the ‘art works’ generated by nature and biologically inspired algorithms can possibly be considered as ‘computationally creative’
Tractable diffusion and coalescent processes for weakly correlated loci
Widely used models in genetics include the Wright-Fisher diffusion and its
moment dual, Kingman's coalescent. Each has a multilocus extension but under
neither extension is the sampling distribution available in closed-form, and
their computation is extremely difficult. In this paper we derive two new
multilocus population genetic models, one a diffusion and the other a
coalescent process, which are much simpler than the standard models, but which
capture their key properties for large recombination rates. The diffusion model
is based on a central limit theorem for density dependent population processes,
and we show that the sampling distribution is a linear combination of moments
of Gaussian distributions and hence available in closed-form. The coalescent
process is based on a probabilistic coupling of the ancestral recombination
graph to a simpler genealogical process which exposes the leading dynamics of
the former. We further demonstrate that when we consider the sampling
distribution as an asymptotic expansion in inverse powers of the recombination
parameter, the sampling distributions of the new models agree with the standard
ones up to the first two orders.Comment: 34 pages, 1 figur
A stochastic large deformation model for computational anatomy
In the study of shapes of human organs using computational anatomy, variations are found to arise from inter-subject anatomical differences, disease-specific effects, and measurement noise. This paper introduces a stochastic model for incorporating random variations into the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. By accounting for randomness in a particular setup which is crafted to fit the geometrical properties of LDDMM, we formulate the template estimation problem for landmarks with noise and give two methods for efficiently estimating the parameters of the noise fields from a prescribed data set. One method directly approximates the time evolution of the variance of each landmark by a finite set of differential equations, and the other is based on an Expectation-Maximisation algorithm. In the second method, the evaluation of the data likelihood is achieved without registering the landmarks, by applying bridge sampling using a stochastically perturbed version of the large deformation gradient flow algorithm. The method and the estimation algorithms are experimentally validated on synthetic examples and shape data of human corpora callosa
Fast, accurate, and transferable many-body interatomic potentials by symbolic regression
The length and time scales of atomistic simulations are limited by the
computational cost of the methods used to predict material properties. In
recent years there has been great progress in the use of machine learning
algorithms to develop fast and accurate interatomic potential models, but it
remains a challenge to develop models that generalize well and are fast enough
to be used at extreme time and length scales. To address this challenge, we
have developed a machine learning algorithm based on symbolic regression in the
form of genetic programming that is capable of discovering accurate,
computationally efficient manybody potential models. The key to our approach is
to explore a hypothesis space of models based on fundamental physical
principles and select models within this hypothesis space based on their
accuracy, speed, and simplicity. The focus on simplicity reduces the risk of
overfitting the training data and increases the chances of discovering a model
that generalizes well. Our algorithm was validated by rediscovering an exact
Lennard-Jones potential and a Sutton Chen embedded atom method potential from
training data generated using these models. By using training data generated
from density functional theory calculations, we found potential models for
elemental copper that are simple, as fast as embedded atom models, and capable
of accurately predicting properties outside of their training set. Our approach
requires relatively small sets of training data, making it possible to generate
training data using highly accurate methods at a reasonable computational cost.
We present our approach, the forms of the discovered models, and assessments of
their transferability, accuracy and speed
Evolution of populations expanding on curved surfaces
The expansion of a population into new habitat is a transient process that
leaves its footprints in the genetic composition of the expanding population.
How the structure of the environment shapes the population front and the
evolutionary dynamics during such a range expansion is little understood. Here,
we investigate the evolutionary dynamics of populations consisting of many
selectively neutral genotypes expanding on curved surfaces. Using a combination
of individual-based off-lattice simulations, geometrical arguments, and
lattice-based stepping-stone simulations, we characterise the effect of
individual bumps on an otherwise flat surface. Compared to the case of a range
expansion on a flat surface, we observe a transient relative increase, followed
by a decrease, in neutral genetic diversity at the population front. In
addition, we find that individuals at the sides of the bump have a dramatically
increased expected number of descendants, while their neighbours closer to the
bump's centre are far less lucky. Both observations can be explained using an
analytical description of straight paths (geodesics) on the curved surface.
Complementing previous studies of heterogeneous flat environments, the findings
here build our understanding of how complex environments shape the evolutionary
dynamics of expanding populations.Comment: This preprint has also been posted to http://www.biorxiv.org with
doi: 10.1101/406280. Seven pages with 5 figures, plus an appendix containing
3 pages with 1 figur
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