A parallel genetic algorithm for the Steiner Problem in Networks

This paper presents a parallel genetic algorithm to the Steiner Problem in Networks. Several previous papers have proposed the adoption of GAs and others metaheuristics to solve the SPN demonstrating the validity of their approaches. This work differs from them for two main reasons: the dimension and the characteristics of the networks adopted in the experiments and the aim from which it has been originated. The reason that aimed this work was namely to build a comparison term for validating deterministic and computationally inexpensive algorithms which can be used in practical engineering applications, such as the multicast transmission in the Internet. On the other hand, the large dimensions of our sample networks require the adoption of a parallel implementation of the Steiner GA, which is able to deal with such large problem instances.

Data assimilation for marine monitoring and prediction: The MERCATOR operational assimilation systems and the MERSEA developments

During the past 15 years, a number of initiatives have been undertaken at national level to develop ocean forecasting systems operating at regional and/or global scales. The co-ordination between these efforts has been organized internationally through the Global Ocean Data Assimilation Experiment (GODAE). The French MERCATOR project is one of the leading participants in GODAE. The MERCATOR systems routinely assimilate a variety of observations such as multi-satellite altimeter data, sea-surface temperature and in situ temperature and salinity profiles, focusing on high-resolution scales of the ocean dynamics. The assimilation strategy in MERCATOR is based on a hierarchy of methods of increasing sophistication including optimal interpolation, Kalman filtering and variational methods, which are progressively deployed through the Syst`eme d’Assimilation MERCATOR (SAM) series. SAM-1 is based on a reduced-order optimal interpolation which can be operated using ‘altimetry-only’ or ‘multi-data’ set-ups; it relies on the concept of separability, assuming that the correlations can be separated into a product of horizontal and vertical contributions. The second release, SAM-2, is being developed to include new features from the singular evolutive extended Kalman (SEEK) filter, such as three-dimensional, multivariate error modes and adaptivity schemes. The third one, SAM-3, considers variational methods such as the incremental four-dimensional variational algorithm. Most operational forecasting systems evaluated during GODAE are based on least-squares statistical estimation assuming Gaussian errors. In the framework of the EU MERSEA (Marine EnviRonment and Security for the European Area) project, research is being conducted to prepare the next-generation operational ocean monitoring and forecasting systems. The research effort will explore nonlinear assimilation formulations to overcome limitations of the current systems. This paper provides an overview of the developments conducted in MERSEA with the SEEK filter, the Ensemble Kalman filter and the sequential importance re-sampling filter.

Synthesis of a systolic array genetic algorithm

The paper presents a design for a hardware genetic algorithm which uses a pipeline of systolic arrays. These arrays have been designed using systolic synthesis techniques which involve expressing the algorithm as a set of uniform recurrence relations. The final design divorces the fitness function evaluation from the hardware and can process chromosomes of different lengths, giving the design a generic quality. The paper demonstrates the design methodology by progressively re-writing a simple genetic algorithm, expressed in C code, into a form from which systolic structures can be deduced. This paper extends previous work by introducing a simplification to a previous systolic design for the genetic algorithm. The simplification results in the removal of 2N 2 + 4N cells and reduces the time complexity by 3N + 1 cycles.

The systolic array genetic algorithm, an example of systolic arrays as a reconfigurable design methodology

We advocate the use of systolic design techniques to create custom hardware for Custom Computing Machines. We have developed a hardware genetic algorithm based on systolic arrays to illustrate the feasibility of the approach. The architecture is independent of the lengths of chromosomes used and can be scaled in size to accommodate different population sizes. An FPGA prototype design can process 16 million genes per second.

The effect of incremental replacement of wheat with soya hulls in diets for Jersey cows on lactational performance, diet digestibility and feeding behaviour

Eight Jersey cows were used in two balanced 4 x 4 Latin Squares to investigate the effects of replacement of dietary starch with non-forage fibre on productivity, diet digestibility and feeding behaviour. Total-mixed rations consisted of maize silage, grass silage and a soyabean meal-based concentrate mixture, each at 250g/kg DM, with the remaining 250g consisting of cracked wheat/soya hulls (SH) in the ratios of 250:0, 167:83; 83:167 and 0:250 g, respectively, for treatments SH0, SH83, SH167 and SH250. Starch concentrations were 302, 248, 193 and 140g/kg DM, and NDF concentrations were 316, 355, 394 and 434g/kg DM, for treatments SHO, SH83, SH167 and SH250, respectively. Total eating time increased (p < 0.05) as SH inclusion increased, but total rumination time was unaffected. Digestibility of DM, organic matter and starch declined (p < 0.01) as SH inclusion increased, whilst digestibility of NDF and ADF increased (p < 0.01). Dry-matter intake tended to decline with increasing SH, whilst bodyweight, milk yield and fat and lactose concentrations were unaffected by treatment. Milk protein concentration decreased (p < 0.01) as SH level increased. Feed conversion efficiency improved (p < 0.05) as SH inclusion rose, but it was not possible to determine whether this was due to the increased fibre levels alone, or the favourable effect on rumen fermentation of decreasing starch levels. (c) 2006 Elsevier B.V. All rights reserved.

Time patterns in UK demand for alcohol and tobacco: an application of the EM algorithm

Capturing the pattern of structural change is a relevant task in applied demand analysis, as consumer preferences may vary significantly over time. Filtering and smoothing techniques have recently played an increasingly relevant role. A dynamic Almost Ideal Demand System with random walk parameters is estimated in order to detect modifications in consumer habits and preferences, as well as changes in the behavioural response to prices and income. Systemwise estimation, consistent with the underlying constraints from economic theory, is achieved through the EM algorithm. The proposed model is applied to UK aggregate consumption of alcohol and tobacco, using quarterly data from 1963 to 2003. Increased alcohol consumption is explained by a preference shift, addictive behaviour and a lower price elasticity. The dynamic and time-varying specification is consistent with the theoretical requirements imposed at each sample point. (c) 2005 Elsevier B.V. All rights reserved.