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.

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.

Consumer acceptance, valuation of and attitudes towards genetically modified food: Review and implications for food policy

An increasing set of evidence has been reported on how consumers could potentially react to the introduction of genetically modified food. Studies typically contain some empirical evidence and some theoretical explanations of the data, however, to date limited effort has been posed on systematically reviewing the existing evidence and its implications for policy. This paper contributes to the literature by bringing together the published evidence on the behavioural frameworks and evidence on the process leading to the public acceptance of genetically modified (GM) food and organisms (GMOs). In doing so, we employ a set of clearly defined search tools and a limited number of comprehensive key words. The study attempts to gather an understanding of the published findings on the determinants of the valuation of GM food - both in terms of willingness to accept and the willing-to-pay a premium for non-GM food, trust with information sources on the safety and public health and ultimate attitudes underpinning such evidence. Furthermore, in the light of such evidence, we formulate some policy strategies to deal with public uncertainly regarding to GMOs and, especially GM food. (c) 2007 Elsevier Ltd. All rights reserved.

The use of non-hypothetical experimental markets for measuring the acceptance of genetically modified foods

The findings from a study measuring consumer acceptance of genetically modified (GM) foods are presented. The empirical data were collected in an experimental market, an approach used extensively in experimental economics for measuring the monetary value of goods. The approach has several advantages over standard approaches used in sensory and marketing research (e.g., surveys and focus groups) because of its non-hypothetical nature and the realism introduced by using real goods, real money, and market discipline. In each of three US locations, we elicited the monetary compensation consumers required to consume a GM food. Providing positive information about the benefits of GM food production, in some cases, reduced the level of monetary compensation demanded to consume the GM food. (C) 2004 Elsevier Ltd. All rights reserved.

Effect of Information about benefits of biotechnology on consumer acceptance of genetically modified food; evidence from experimental auctions in the United States, England, and France

This study investigates the effect of information about potential benefits of biotechnology on consumer acceptance of genetically modified (GM) foods. Consumer willingness to accept compensation to consume a GM food was elicited using an incentive compatible auction mechanism in three US states (California, Florida, and Texas) and in two European countries (England and France). Results indicate that information on environmental benefits, health benefits and benefits to the third world significantly decreased the amount of money consumers demanded to consume GM food; however, the effect of information varied by type of information and location. Consistent with prior research, we find that initial attitudes toward biotechnology have a significant effect on how individuals responded to new information.

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.

Use of a novel Hill-climbing genetic algorithm in protein folding simulations

We have developed a novel Hill-climbing genetic algorithm (GA) for simulation of protein folding. The program (written in C) builds a set of Cartesian points to represent an unfolded polypeptide's backbone. The dihedral angles determining the chain's configuration are stored in an array of chromosome structures that is copied and then mutated. The fitness of the mutated chain's configuration is determined by its radius of gyration. A four-helix bundle was used to optimise simulation conditions, and the program was compared with other, larger, genetic algorithms on a variety of structures. The program ran 50% faster than other GA programs. Overall, tests on 100 non-redundant structures gave comparable results to other genetic algorithms, with the Hill-climbing program running from between 20 and 50% faster. Examples including crambin, cytochrome c, cytochrome B and hemerythrin gave good secondary structure fits with overall alpha carbon atom rms deviations of between 5 and 5.6 Angstrom with an optimised hydrophobic term in the fitness function. (C) 2003 Elsevier Ltd. All rights reserved.

Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction

Liquid chromatography-mass spectrometry (LC-MS) datasets can be compared or combined following chromatographic alignment. Here we describe a simple solution to the specific problem of aligning one LC-MS dataset and one LC-MS/MS dataset, acquired on separate instruments from an enzymatic digest of a protein mixture, using feature extraction and a genetic algorithm. First, the LC-MS dataset is searched within a few ppm of the calculated theoretical masses of peptides confidently identified by LC-MS/MS. A piecewise linear function is then fitted to these matched peptides using a genetic algorithm with a fitness function that is insensitive to incorrect matches but sufficiently flexible to adapt to the discrete shifts common when comparing LC datasets. We demonstrate the utility of this method by aligning ion trap LC-MS/MS data with accurate LC-MS data from an FTICR mass spectrometer and show how hybrid datasets can improve peptide and protein identification by combining the speed of the ion trap with the mass accuracy of the FTICR, similar to using a hybrid ion trap-FTICR instrument. We also show that the high resolving power of FTICR can improve precision and linear dynamic range in quantitative proteomics. The alignment software, msalign, is freely available as open source.

Variable step LMS algorithm using the accumulated instantaneous error concept

The convergence speed of the standard Least Mean Square adaptive array may be degraded in mobile communication environments. Different conventional variable step size LMS algorithms were proposed to enhance the convergence speed while maintaining low steady state error. In this paper, a new variable step LMS algorithm, using the accumulated instantaneous error concept is proposed. In the proposed algorithm, the accumulated instantaneous error is used to update the step size parameter of standard LMS is varied. Simulation results show that the proposed algorithm is simpler and yields better performance than conventional variable step LMS.