Intelligent Genetic Algorithms in Evolutionary Computation Part 1. Biological Foundation

In this paper, we review a large amount of historical biological literature [Darwin, 1862, 1871; Fisher, 1930 and others] and recent developments in biological [ Anderson, 1994] and biocomputational literature [Miller & Todd, 1992, 1994], try to integrate the dynamics of interplay between natural selection and sexual selection through mate choice in biology with evolutionary computation as a process of search, diversification and optimization and originate a new class of evolutionary algorithm which we term Intelligent Genetic Algorithms. These intelligent genetic algorithms demonstrate their effectiveness and efficiency in generating evolutionary innovations, maintaining genetic diversity, promoting mate choice and sexual recombination in species and guiding the movement of a population from local optima to global optima in parallel. Furthermore, we attempt to provide some common biological origins for these new Intelligent Genetic Algorithms

A Genetic Programming Methodology for the Solution of the Cell-Formation Problem

The problem of identifying machine cells and corresponding part families in cellular manufacturing has been extensively researched over the last thirty years. However, the complexity of the problem and the considerable number of issues involved in its solution create the need for increasingly efficient algorithms. In this report we investigate the use of Genetic Programming for the solution of a simple version of the problem. Genetic Programming is initially employed to attack individual cell formation problems. In a second stage, Genetic Programming evolves a similarity coefficient for the solution of any cell-formation problem

Fitness Distance Correlation as a Measure of GA Performance

In this paper, the mathematical interpretation of correlation coefficient is reviewed to explain the conditions under which it operates. Using the work of Jones and Forrest (1995) on fitness distance correlation (FDC) as a measure of problem difficulty for genetic algorithms, a novel framework combing FDC with the Experimental Design perspective in statistics is proposed. It is shown that this method not only satisfies the mathematical condition of correlation coefficient, but alson that it is closely relevant to genetic operators such as crossover and mutation and can therefore be used to predict the performance of genetic algorithms more accurately. Different well-known problems such as epistasis interactions, isolation or needle-in-a-haystack, high fitness variance,deceptiveness and multimodality, which make the GA search process difficult, are investigated. Experimental results show that this framework is an effective metric for GA performance on the fitness landscape and offers useful guidance in constructing efficient genetic algorithms

Discriminating between disease-causing and neutral non-frameshifting micro-INDELs by support vector machines by means of integrated sequence- and structure-based features

poster abstractMicro-INDELs (insertions or deletions of ≤20 bp) constitute the second most frequent class of human gene mutation after single nucleotide variants. A significant portion of exonic INDELs are non-frameshifting (NFS), serving to insert or delete a discrete number of amino-acid residues. Despite the relative abundance of NFS-INDELs, their damaging effect on protein structure and function has gone largely unstudied whilst bioinformatics tools for discriminating between disease-causing and neutral NFS-INDELs remain to be developed. We have developed such a technique (DDIG-in; Detecting DIsease-causing Genetic variations due to INDELs) by comparing the properties of disease-causing NFS-INDELs from the Human Gene Mutation Database (HGMD) with putatively neutral NFS-INDELs from the 1,000 Genomes Project. Having considered 58 different sequence- and structure-based features, we found that predicted disordered regions around the NFS-INDEL region had the highest discriminative capability (disease versus neutral) with an Area Under the receiver-operating characteristic Curve (AUC) of 0.82 and a Matthews Correlation Coefficient (MCC) of 0.56. All features studied were combined by support vector machines (SVM) and selected by a greedy algorithm. The resulting SVM models were trained and tested by ten-fold cross-validation on the microdeletion dataset and independently tested on the microinsertion dataset and vice versa. The final SVM model for determining NFS-INDEL disease-causing probability was built on non-redundant datasets with a protein sequence identity cutoff of 35% and yielded an MCC value of 0.68, an accuracy of 84% and an AUC of 0.89. Predicted disease-causing probabilities exhibited a strong negative correlation with the average minor allele frequency (correlation coefficient, -0.84). DDIG-in, available at http://sparks.informatics.iupui.edu, can be used to estimate the disease-causing probability for a given NFS-INDEL

The Human Gene Mutation Database: 2008 update

The Human Gene Mutation Database (HGMD®) is a comprehensive core collection of germline mutations in nuclear genes that underlie or are associated with human inherited disease. Here, we summarize the history of the database and its current resources. By December 2008, the database contained over 85,000 different lesions detected in 3,253 different genes, with new entries currently accumulating at a rate exceeding 9,000 per annum. Although originally established for the scientific study of mutational mechanisms in human genes, HGMD has since acquired a much broader utility for researchers, physicians, clinicians and genetic counselors as well as for companies specializing in biopharmaceuticals, bioinformatics and personalized genomics. HGMD was first made publicly available in April 1996, and a collaboration was initiated in 2006 between HGMD and BIOBASE GmbH. This cooperative agreement covers the exclusive worldwide marketing of the most up-to-date (subscription) version of HGMD, HGMD Professional, to academic, clinical and commercial users

Beyond 'Global Production Networks': Australian Fashion Week's Trans-Sectoral Synergies

When studies of industrial organisation are informed by commodity chain, actor network, or global production network theories and focus on tracing commodity flows, social networks, or a combination of the two, they can easily overlook the less routine trans-sectoral associations that are crucial to the creation and realisation of value. This paper shifts attention to identifying the sites at which diverse specialisations meet to concentrate and amplify mutually reinforcing circuits of value. These valorisation processes are demonstrated in the case of Australian Fashion Week, an event in which multiple interests converge to synchronize different expressions of fashion ideas, actively construct fashion markets and enhance the value of a diverse range of fashionable commodities. Conceptualising these interconnected industries as components of a trans-sectoral fashion complex has implications for understanding regional development, world cities, production location, and the manner in which production systems “touch down” in different places

5-fluorouracil and folinic acid-induced mucositis: no effect of oral glutamine supplementation.

In some clinical situations the endogenous production of glutamine may be insufficient to maintain optimal tissue structure and function such that glutamine becomes a conditionally essential amino acid. Studies in laboratory animals have demonstrated that glutamine supplementation can reduce the incidence and severity of cytotoxic-induced mucositis. This study examined the role of oral glutamine supplementation in the management of mucositis caused by 5-fluorouracil (5-FU) and folinic acid. Twenty-eight patients with gastrointestinal cancers were randomised to receive 16 g of glutamine per day for 8 days, or placebo, in a randomised double-blind trial before crossing over to the alternative supplement during the second treatment cycle. The supplement was well tolerated with no apparent adverse effects, but failed to have any significant effect on oral mucositis assessed by the patients or investigator. The possible reasons for this apparent lack of benefit are discussed

The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies

The Human Gene Mutation Database (HGMD®) constitutes a comprehensive collection of published germline mutations in nuclear genes that underlie, or are closely associated with human inherited disease. At the time of writing (March 2017), the database contained in excess of 203,000 different gene lesions identified in over 8000 genes manually curated from over 2600 journals. With new mutation entries currently accumulating at a rate exceeding 17,000 per annum, HGMD represents de facto the central unified gene/disease-oriented repository of heritable mutations causing human genetic disease used worldwide by researchers, clinicians, diagnostic laboratories and genetic counsellors, and is an essential tool for the annotation of next-generation sequencing data. The public version of HGMD (http://www.hgmd.org) is freely available to registered users from academic institutions and non-profit organisations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via QIAGEN Inc

Analysing the impact of far-out sidelobes on the imaging performance of the SKA-LOW telescope

The Square Kilometre Array’s Low Frequency instrument (SKA-LOW) will operate in the undersampled regime for most of the frequency band where grating lobes pose particular challenges. To achieve the expected level of sensitivity for SKA-LOW, it is particularly important to understand how interfering sources in both near and far side-lobes of the station beam affect the imaging performance. In this study, we discuss options for station designs, and adopting a random element layout, we assess its effectiveness by investigating how sources far from the main lobe of the station beam degrade images of the target field. These sources have the effect of introducing a noise-like corruption to images, which is called the far sidelobe confusion noise (FSCN). Using $\tiny{OSKAR}$, a software simulator accelerated using graphics processing units, we carried out end-to-end simulations using an all-sky model and telescope configuration representative of the SKA-LOW instrument. The FSCN is a function of both the station beam and the interferometric point spread function, and decreases with increasing observation time until the coverage of the aperture plane no longer improves. Using apodization to reduce the level of near-in sidelobes of the station beam had a notable improvement on the level of the FSCN at low frequencies. Our results indicate that the effects of picking up sources in the sidelobes are worse at low frequencies, where the array is less sparse.This work used the Wilkes GPU cluster at the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/), provided by Dell Inc., NVIDIA and Mellanox, and part funded by STFC with industrial sponsorship from Rolls Royce and Mitsubishi Heavy Industries