A Dynamic Migration Model for Self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

A Dynamic Migration Model for self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

Towards a Dynamic Communication Model: A Useful Tool in Relationship Communication

In order to bring insight into the emerging concept of relationship communication, concepts from two research traditions will be combined in this paper. Based on those concepts a new model, the dynamic relationship communication model, will be presented. Instead of a company perspective focusing on the integration of outgoing messages such as advertising, public relations and sales activities, it is suggested that the focus should be on factors integrated by the receiver. Such factors can be historical, future, external and internal factors. Thus, the model put a strong focus on the receiver in the communication process. The dynamic communication model is illustrated empirically using it as a tool on 78 short stories about communication. The empirical findings show that relationship communication occurs in some cases; in some cases it does not occur. The model is a useful tool in displaying relationship communication and how it differs from other communication. The importance of the time dimension, historical and future factors, in relationship communications is discussed. The possibility of reducing communications costs by the notion of relationship communication is discussed in managerial implications.

A dynamic coupling model for hybrid atomistic-continuum computations

A dynamic coupling model is developed for a hybrid atomistic-continuum computation in micro- and nano-fluidics. In the hybrid atomistic-continuum computation, a molecular dynamics (MD) simulation is utilized in one region where the continuum assumption breaks down and the Navier-Stokes (NS) equations are used in another region where the continuum assumption holds. In the overlapping part of these two regions, a constrained particle dynamics is needed to couple the MD simulation and the NS equations. The currently existing coupling models for the constrained particle dynamics have a coupling parameter, which has to be empirically determined. In the present work, a novel dynamic coupling model is introduced where the coupling parameter can be calculated as the computation progresses rather than inputing a priori. The dynamic coupling model is based on the momentum constraint and exhibits a correct relaxation rate. The results from the hybrid simulation on the Couette flow and the Stokes flow are in good agreement with the data from the full MD simulation and the solutions of the NS equations, respectively. (c) 2007 Elsevier Ltd. All rights reserved.

Response of the Bucket Foundation in Calcareous Sand under Horizontal Dynamic Load

Abstract: Experiments to determine the horizontal static bearing capacity are carried out first. The static bearing capacity is a reference for choosing the amplitudes of dynamic load. Then a series of experiments under dynamic horizontal load are carried out in laboratory to study the influences of factors, such as the scales of bucket, the amplitude and frequency of load, the density of soils etc.. The responses of bucket foundations in calcareous sand under horizontal dynamic load are analyzed according to the experimental results. The displacements of bucket and sand layer are analyzed.

Dimensionless Analysis of the Simulation of Bucket Foundations under Dynamic Load

Firstly, the main factors are obtained by use of dimensionless analysis. Secondly, the time scaling factors in centrifuge modeling of bucket foundations under dynamic load are analyzed based on dimensionless analysis and control- ling equation. A simplified method for dealing with the conflict of scaling factors of the inertial and the percolation in sand foundation is presented. The presented method is that the material for experiments is not changed while the effects are modified by perturbation method. Thirdly, the characteristic time of liquefaction state and the characteristic scale of affected zone are analyzed.

Bayesian interpolation in a dynamic sinusoidal model with application to packet-loss concealment

In this paper, we consider Bayesian interpolation and parameter estimation in a dynamic sinusoidal model. This model is more flexible than the static sinusoidal model since it enables the amplitudes and phases of the sinusoids to be time-varying. For the dynamic sinusoidal model, we derive a Bayesian inference scheme for the missing observations, hidden states and model parameters of the dynamic model. The inference scheme is based on a Markov chain Monte Carlo method known as Gibbs sampler. We illustrate the performance of the inference scheme to the application of packet-loss concealment of lost audio and speech packets. © EURASIP, 2010.

XBATTLE: A Dynamic Distributed Model of Generalized Military Conflict

A dynamic distributed model is presented that reproduces the dynamics of a wide range of varied battle scenarios with a general and abstract representation. The model illustrates the rich dynamic behavior that can be achieved from a simple generic model.

A Dynamic Directional Model for Effective Brain Connectivity using Electrocorticographic (ECoG) Time Series.

We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on intracranial electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components (state equations), and observation equations linking the underlying neuronal states to observed data. When applied to functional MRI or EEG data, DDMs usually have complex formulations and thus can accommodate only a few regions, due to limitations in spatial resolution and/or temporal resolution of these imaging modalities. In contrast, we formulate our model in the context of ECoG data. The combined high temporal and spatial resolution of ECoG data result in a much simpler DDM, allowing investigation of complex connections between many regions. To identify functionally segregated sub-networks, a form of biologically economical brain networks, we propose the Potts model for the DDM parameters. The neuronal states of brain components are represented by cubic spline bases and the parameters are estimated by minimizing a log-likelihood criterion that combines the state and observation equations. The Potts model is converted to the Potts penalty in the penalized regression approach to achieve sparsity in parameter estimation, for which a fast iterative algorithm is developed. The methods are applied to an auditory ECoG dataset.

An automatable generic strategy for dynamic load balancing in parallel structured mesh CFD codes

Parallel computing is now widely used in numerical simulation, particularly for application codes based on finite difference and finite element methods. A popular and successful technique employed to parallelize such codes onto large distributed memory systems is to partition the mesh into sub-domains that are then allocated to processors. The code then executes in parallel, using the SPMD methodology, with message passing for inter-processor interactions. In order to improve the parallel efficiency of an imbalanced structured mesh CFD code, a new dynamic load balancing (DLB) strategy has been developed in which the processor partition range limits of just one of the partitioned dimensions uses non-coincidental limits, as opposed to coincidental limits. The ‘local’ partition limit change allows greater flexibility in obtaining a balanced load distribution, as the workload increase, or decrease, on a processor is no longer restricted by the ‘global’ (coincidental) limit change. The automatic implementation of this generic DLB strategy within an existing parallel code is presented in this chapter, along with some preliminary results.

Parallel CFD fire modelling on office PCs with dynamic load balancing

Parallel processing techniques have been used in the past to provide high performance computing resources for activities such as Computational Fluid Dynamics. This is normally achieved using specialized hardware and software, the expense of which would be difficult to justify for many fire engineering practices. In this paper, we demonstrate how typical office-based PCs attached to a local area network have the potential to offer the benefits of parallel processing with minimal costs associated with the purchase of additional hardware or software. A dynamic load balancing scheme was devised to allow the effective use of the software on heterogeneous PC networks. This scheme ensured that the impact between the parallel processing task and other computer users on the network was minimized thus allowing practical parallel processing within a conventional office environment. Copyright © 2006 John Wiley & Sons, Ltd.