Dynamic Niche Clustering: a fuzzy variable radius niching technique for multimodal optimisation in GAs

This paper describes the recent developments and improvements made to the variable radius niching technique called Dynamic Niche Clustering (DNC). DNC is fitness sharing based technique that employs a separate population of overlapping fuzzy niches with independent radii which operate in the decoded parameter space, and are maintained alongside the normal GA population. We describe a speedup process that can be applied to the initial generation which greatly reduces the complexity of the initial stages. A split operator is also introduced that is designed to counteract the excessive growth of niches, and it is shown that this improves the overall robustness of the technique. Finally, the effect of local elitism is documented and compared to the performance of the basic DNC technique on a selection of 2D test functions. The paper is concluded with a view to future work to be undertaken on the technique.

Numerical Methods for Stiff Two-Point Boundary Value Problems

We consider the two-point boundary value problem for stiff systems of ordinary differential equations. For systems that can be transformed to essentially diagonally dominant form with appropriate smoothness conditions, a priori estimates are obtained. Problems with turning points can be treated with this theory, and we discuss this in detail. We give robust difference approximations and present error estimates for these schemes. In particular we give a detailed description of how to transform a general system to essentially diagonally dominant form and then stretch the independent variable so that the system will satisfy the correct smoothness conditions. Numerical examples are presented for both linear and nonlinear problems.

Host selection and performance of the giant willow aphid, Tuberolachnus salignus (Gmelin) - Implications for pest management

1 The recent increase in planting of selected willow clones as energy crops for biomass production has resulted in a need to understand the relationship between commonly grown, clonally propagated genotypes and their pests. 2 For the first time, we present a study of the interactions of six willow clones and a previously unconsidered pest, the giant willow aphid Tuberolachnus salignus. 3 Tuberolachnus salignus alatae displayed no preference between the clones, but there was genetic variation in resistance between the clones; Q83 was the most resistant and led to the lowest reproductive performance in the aphid 4 Maternal effects buffered changes in aphid performance. On four tested willow clones fecundity of first generation aphids on the new host clone was intermediate to that of the second generation and that of the clone used to maintain the aphids in culture. 5 In the field, patterns of aphid infestation were highly variable between years, with the duration of attack being up to four times longer in 1999. In both years there was a significant effect of willow clone on the intensity of infestation. However, whereas Orm had the lowest intensity of infestation in the first year, Dasyclados supported a lower population level than other monitored clones in the second year.

Selection of potential probiotic lactic acid bacteria from fermented olives by in vitro tests

The present study aims to evaluate the probiotic potential of lactic acid bacteria (LAB) isolated from naturally fermented olives and select candidates to be used as probiotic starters for the improvement of the traditional fermentation process and the production of newly added value functional foods. Seventy one (71) lactic acid bacterial strains (17 Leuconostoc mesenteroides, 1 Ln. pseudomesenteroides, 13 Lactobacillus plantarum, 37 Lb. pentosus, 1 Lb. paraplantarum, and 2 Lb. paracasei subsp. paracasei) isolated from table olives were screened for their probiotic potential. Lb. rhamnosus GG and Lb. casei Shirota were used as reference strains. The in vitro tests included survival in simulated gastrointestinal tract conditions, antimicrobial activity (against Listeria monocytogenes, Salmonella Enteritidis, Escherichia coli O157:H7), Caco-2 surface adhesion, resistance to 9 antibiotics and haemolytic activity. Three (3) Lb. pentosus, 4 Lb. plantarum and 2 Lb. paracasei subsp. paracasei strains demonstrated the highest final population (>8 log cfu/ml) after 3 h of exposure at low pH. The majority of the tested strains were resistant to bile salts even after 4 h of exposure, while 5 Lb. plantarum and 7 Lb. pentosus strains exhibited partial bile salt hydrolase activity. None of the strains inhibited the growth of the pathogens tested. Variable efficiency to adhere to Caco-2 cells was observed. This was the same regarding strains' susceptibility towards different antibiotics. None of the strains exhibited β-haemolytic activity. As a whole, 4 strains of Lb. pentosus, 3 strains of Lb. plantarum and 2 strains of Lb. paracasei subsp. paracasei were found to possess desirable in vitro probiotic properties similar to or even better than the reference probiotic strains Lb. casei Shirota and Lb. rhamnosus GG. These strains are good candidates for further investigation both with in vivo studies to elucidate their potential health benefits and in olive fermentation processes to assess their technological performance as novel probiotic starters.

PDFOS: PDF estimation based over-sampling for imbalanced two-class problems

This contribution proposes a novel probability density function (PDF) estimation based over-sampling (PDFOS) approach for two-class imbalanced classification problems. The classical Parzen-window kernel function is adopted to estimate the PDF of the positive class. Then according to the estimated PDF, synthetic instances are generated as the additional training data. The essential concept is to re-balance the class distribution of the original imbalanced data set under the principle that synthetic data sample follows the same statistical properties. Based on the over-sampled training data, the radial basis function (RBF) classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier’s structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed PDFOS approach is demonstrated by the empirical study on several imbalanced data sets.

A radial basis function network classifier to maximise leave-one-out mutual information

tWe develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF)network classifiers for two-class problems. Our approach integrates several concepts in probabilisticmodelling, including cross validation, mutual information and Bayesian hyperparameter fitting. At eachstage of the OFS procedure, one model term is selected by maximising the leave-one-out mutual infor-mation (LOOMI) between the classifier’s predicted class labels and the true class labels. We derive theformula of LOOMI within the OFS framework so that the LOOMI can be evaluated efficiently for modelterm selection. Furthermore, a Bayesian procedure of hyperparameter fitting is also integrated into theeach stage of the OFS to infer the l2-norm based local regularisation parameter from the data. Since eachforward stage is effectively fitting of a one-variable model, this task is very fast. The classifier construc-tion procedure is automatically terminated without the need of using additional stopping criterion toyield very sparse RBF classifiers with excellent classification generalisation performance, which is par-ticular useful for the noisy data sets with highly overlapping class distribution. A number of benchmarkexamples are employed to demonstrate the effectiveness of our proposed approach.

The road not taken: observing a process of issue selection and model conceptualization

This article describes a case study involving information technology managers and their new programmer recruitment policy, but the primary interest is methodological. The processes of issue generation and selection and model conceptualization are described. Early use of “magnetic hexagons” allowed the generation of a range of issues, most of which would not have emerged if system dynamics elicitation techniques had been employed. With the selection of a specific issue, flow diagraming was used to conceptualize a model, computer implementation and scenario generation following naturally. Observations are made on the processes of system dynamics modeling, particularly on the need to employ general techniques of knowledge elicitation in the early stages of interventions. It is proposed that flexible approaches should be used to generate, select, and study the issues, since these reduce any biasing of the elicitation toward system dynamics problems and also allow the participants to take up the most appropriate problem- structuring approach.

Essays on robust portfolio selection and pension finance

This thesis examines three different, but related problems in the broad area of portfolio management for long-term institutional investors, and focuses mainly on the case of pension funds. The first idea (Chapter 3) is the application of a novel numerical technique – robust optimization – to a real-world pension scheme (the Universities Superannuation Scheme, USS) for first time. The corresponding empirical results are supported by many robustness checks and several benchmarks such as the Bayes-Stein and Black-Litterman models that are also applied for first time in a pension ALM framework, the Sharpe and Tint model and the actual USS asset allocations. The second idea presented in Chapter 4 is the investigation of whether the selection of the portfolio construction strategy matters in the SRI industry, an issue of great importance for long term investors. This study applies a variety of optimal and naïve portfolio diversification techniques to the same SRI-screened universe, and gives some answers to the question of which portfolio strategies tend to create superior SRI portfolios. Finally, the third idea (Chapter 5) compares the performance of a real-world pension scheme (USS) before and after the recent major changes in the pension rules under different dynamic asset allocation strategies and the fixed-mix portfolio approach and quantifies the redistributive effects between various stakeholders. Although this study deals with a specific pension scheme, the methodology can be applied by other major pension schemes in countries such as the UK and USA that have changed their rules.