Synapsing variable length crossover: An algorithm for crossing and comparing variable length genomes

The Synapsing Variable Length Crossover (SVLC) algorithm provides a biologically inspired method for performing meaningful crossover between variable length genomes. In addition to providing a rationale for variable length crossover it also provides a genotypic similarity metric for variable length genomes enabling standard niche formation techniques to be used with variable length genomes. Unlike other variable length crossover techniques which consider genomes to be rigid inflexible arrays and where some or all of the crossover points are randomly selected, the SVLC algorithm considers genomes to be flexible and chooses non-random crossover points based on the common parental sequence similarity. The SVLC Algorithm recurrently "glues" or synapses homogenous genetic sub-sequences together. This is done in such a way that common parental sequences are automatically preserved in the offspring with only the genetic differences being exchanged or removed, independent of the length of such differences. In a variable length test problem the SVLC algorithm is shown to outperform current variable length crossover techniques. The SVLC algorithm is also shown to work in a more realistic robot neural network controller evolution application.

Synapsing variable-length crossover: Meaningful crossover for variable-length genomes

The synapsing variable-length crossover (SVLC algorithm provides a biologically inspired method for performing meaningful crossover between variable-length genomes. In addition to providing a rationale for variable-length crossover, it also provides a genotypic similarity metric for variable-length genomes, enabling standard niche formation techniques to be used with variable-length genomes. Unlike other variable-length crossover techniques which consider genomes to be rigid inflexible arrays and where some or all of the crossover points are randomly selected, the SVLC algorithm considers genomes to be flexible and chooses non-random crossover points based on the common parental sequence similarity. The SVLC algorithm recurrently "glues" or synapses homogenous genetic subsequences together. This is done in such a way that common parental sequences are automatically preserved in the offspring with only the genetic differences being exchanged or removed, independent of the length of such differences. In a variable-length test problem, the SVLC algorithm compares favorably with current variable-length crossover techniques. The variable-length approach is further advocated by demonstrating how a variable-length genetic algorithm (GA) can obtain a high fitness solution in fewer iterations than a traditional fixed-length GA in a two-dimensional vector approximation task.

An efficient shock capturing algorithm for compressible flows in a duct of variable cross-section

A numerical scheme is presented for the solution of the Euler equations of compressible flow of a gas in a single spatial co-ordinate. This includes flow in a duct of variable cross-section as well as flow with slab, cylindrical or spherical symmetry and can prove useful when testing codes for the two-dimensional equations governing compressible flow of a gas. The resulting scheme requires an average of the flow variables across the interface between cells and for computational efficiency this average is chosen to be the arithmetic mean, which is in contrast to the usual ‘square root’ averages found in this type of scheme. The scheme is applied with success to five problems with either slab or cylindrical symmetry and a comparison is made in the cylindrical case with results from a two-dimensional problem with no sources.

Adapting to climate change impacts on water resources in England - an assessment of draft Water Resources Management Plans

Climate change is expected to produce reductions in water availability in England, potentially necessitating adaptive action by the water industry to maintain supplies. As part of Ofwat's fifth Periodic Review (PR09), water companies recently released their draft Water Resources Management Plans, setting out how each company intends to maintain the balance between the supply and demand for water over the next 25 years, following Environment Agency guidelines. This paper reviews these plans to determine company estimates of the impact of climate change on water supply relative to other resource pressures. The approaches adopted for incorporating the impact in the plans and the proposed management solutions are also identified. Climate change impacts for individual resource zones range from no reductions in deployable output to greater than 50% over the planning period. The estimated national aggregated loss of deployable output under a “core” climate scenario is ~520 Ml/d (3% of deployable output) by 2034/35, the equivalent of the supply of one entire water company (South West Water). Climate change is the largest single driver of change in water supplies over the planning period. Over half of the climate change impact is concentrated in southern England. In extreme cases, climate change uncertainty is of the same magnitude as the change under the core scenario (up to a loss of ~475 Ml/d). 44 of the 68 resource zones with available data are estimated to have a climate change impact. In 35 of these climate change has the greatest impact although in 10 zones sustainability reductions have a greater impact. Of the overall change in downward pressure on the supply-demand balance over the planning period, ~56% is accounted for by increased demand (620 Ml/d) and supply side climate change accounts for ~37% (407 Ml/d). Climate change impacts have a cumulative impact in concert with other changing supply side reducing components increasing the national pressure on the supply-demand balance. Whilst the magnitude of climate change appears to justify its explicit consideration, it is rare that adaptation options are planned solely in response to climate change but as a suite of options to provide a resilient supply to a range of pressures (including significant demand side pressures). Supply-side measures still tend to be considered by water companies to be more reliable than demand-side measures.

Local versus organic: a turn in consumer preferences and willingness-to-pay

Demand for local food in the United States has significantly increased over the last decade. In an attempt to understand the drivers of this demand and how they have changed over time, we investigate the literature on organic and local foods over the last few decades. We focus our review on studies that allow comparison of characteristics now associated with both local and organic food. We summarize the major findings of these studies and their implications for understanding drivers of local food demand. Prior to the late 1990s, most studies failed to consider factors now associated with local food, and the few that included these factors found very little support for them. In many cases, the lines between local and organic were blurred. Coincident with the development of federal organic food standards, studies began to find comparatively more support for local food as distinct and separate from organic food. Our review uncovers a distinct turn in the demand for local and organic food. Before the federal organic standards, organic food was linked to small farms, animal welfare, deep sustainability, community support, and many other factors that are not associated with most organic foods today. Based on our review, we argue that demand for local food arose largely in response to corporate cooptation of the organic food market and the arrival of “organic lite.” This important shift in consumer preferences away from organic and toward local food has broad implications for the environment and society. If these patterns of consumer preferences prove to be sustainable, producers, activists, and others should be aware of the implications that these trends have for the food system at large.

Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks

An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models. The algorithm is an extension of a forward modified Gram-Schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting. The proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice-based modeling algorithms such as radial basis function neurofuzzy networks, enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation. Some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm.

Farm animal welfare, consumer willingness to pay, and trust: results of a cross-national survey

Higher animal welfare standards increase costs along the supply chain of certified animal-friendly products (AFP). Since the market outcome of certified AFP depends on consumer confidence toward supply chain operators complying with these standards, the role of trust in consumer willingness-to-pay (WTP) for AFP is paramount. Results from a contingent valuation survey administered in five European Union countries show that WTP estimates were sensitive to robust measures of consumer trust for certified AFP. Deriving the WTP effect of a single food category on total food expenditure is difficult for survey respondents; hence, a budget approach was employed to facilitate this process.

An LMS style variable tap-length algorithm for structure adaptation

Searching for the optimum tap-length that best balances the complexity and steady-state performance of an adaptive filter has attracted attention recently. Among existing algorithms that can be found in the literature, two of which, namely the segmented filter (SF) and gradient descent (GD) algorithms, are of particular interest as they can search for the optimum tap-length quickly. In this paper, at first, we carefully compare the SF and GD algorithms and show that the two algorithms are equivalent in performance under some constraints, but each has advantages/disadvantages relative to the other. Then, we propose an improved variable tap-length algorithm using the concept of the pseudo fractional tap-length (FT). Updating the tap-length with instantaneous errors in a style similar to that used in the stochastic gradient [or least mean squares (LMS)] algorithm, the proposed FT algorithm not only retains the advantages from both the SF and the GD algorithms but also has significantly less complexity than existing algorithms. Both performance analysis and numerical simulations are given to verify the new proposed algorithm.

Variable selection for financial distress classification using a genetic algorithm

This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model and minimizing the collinearity between the model inputs. A case study involving 60 failed and continuing British firms in the period 1997-2000 is used for illustration. The classification model based on ratios selected by the genetic algorithm compares favorably with a model employing ratios usually found in the financial distress literature.