Hybridisation of Neural Networks and Genetic Algorithms in an Application of Time-Optimal Control

This paper presents the use of neural network and genetic algorithms in the time-optimal control of a closed loop robotics system. Radial basis function networks are used in conjunction with PID controllers in an independent joint position control to reduce tracking error. Genetic algorithm is then used to solve a multi-objective optimisation problem where decision variables are torque limits on each joint and the objective variables are trajectory time and position tracking error. This represents a task hybridisation between neural network and genetic algorithm. Two approaches with genetic algorithms are used to solve this optimisation problem: Multi-objective Genetic Algorithm (MOGA) and genetic algorithm with weighted-sum approach

Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses

<p>Abstract</p> <p>Background</p> <p>Purely epistatic multi-locus interactions cannot generally be detected via single-locus analysis in case-control studies of complex diseases. Recently, many two-locus and multi-locus analysis techniques have been shown to be promising for the epistasis detection. However, exhaustive multi-locus analysis requires prohibitively large computational efforts when problems involve large-scale or genome-wide data. Furthermore, there is no explicit proof that a combination of multiple two-locus analyses can lead to the correct identification of multi-locus interactions.</p> <p>Results</p> <p>The proposed 2LOmb algorithm performs an omnibus permutation test on ensembles of two-locus analyses. The algorithm consists of four main steps: two-locus analysis, a permutation test, global <it>p</it>-value determination and a progressive search for the best ensemble. 2LOmb is benchmarked against an exhaustive two-locus analysis technique, a set association approach, a correlation-based feature selection (CFS) technique and a tuned ReliefF (TuRF) technique. The simulation results indicate that 2LOmb produces a low false-positive error. Moreover, 2LOmb has the best performance in terms of an ability to identify all causative single nucleotide polymorphisms (SNPs) and a low number of output SNPs in purely epistatic two-, three- and four-locus interaction problems. The interaction models constructed from the 2LOmb outputs via a multifactor dimensionality reduction (MDR) method are also included for the confirmation of epistasis detection. 2LOmb is subsequently applied to a type 2 diabetes mellitus (T2D) data set, which is obtained as a part of the UK genome-wide genetic epidemiology study by the Wellcome Trust Case Control Consortium (WTCCC). After primarily screening for SNPs that locate within or near 372 candidate genes and exhibit no marginal single-locus effects, the T2D data set is reduced to 7,065 SNPs from 370 genes. The 2LOmb search in the reduced T2D data reveals that four intronic SNPs in <it>PGM1 </it>(phosphoglucomutase 1), two intronic SNPs in <it>LMX1A </it>(LIM homeobox transcription factor 1, alpha), two intronic SNPs in <it>PARK2 </it>(Parkinson disease (autosomal recessive, juvenile) 2, parkin) and three intronic SNPs in <it>GYS2 </it>(glycogen synthase 2 (liver)) are associated with the disease. The 2LOmb result suggests that there is no interaction between each pair of the identified genes that can be described by purely epistatic two-locus interaction models. Moreover, there are no interactions between these four genes that can be described by purely epistatic multi-locus interaction models with marginal two-locus effects. The findings provide an alternative explanation for the aetiology of T2D in a UK population.</p> <p>Conclusion</p> <p>An omnibus permutation test on ensembles of two-locus analyses can detect purely epistatic multi-locus interactions with marginal two-locus effects. The study also reveals that SNPs from large-scale or genome-wide case-control data which are discarded after single-locus analysis detects no association can still be useful for genetic epidemiology studies.</p

Myoelectric Signals Pattern Recognition for Intelligent Functional Operation of Upper-Limb Prosthesis

This paper represents a comparative study of the classification accuracy of myoelectic signals using multi-layer perceptron with back-propagation algorithm and radial basis functions networks. The myoelectric signals considered are used to classify four upper-limb movements which are elbow bending, elbow extension, wrist pronation and wrist supination. The network structure for multi-layer perceptron is a fully connected one, while the structures used in radial basis functions network are both fully connected and partially connected. Two learning strategies are used for training radial basis networks, namely supervised selection of centres and fixed centres selected at random. The results suggest that radial-basis function network with fixed centres can generalise better than the others without enquiring extra computational effort

GALAXY: A new hybrid MOEA for the Optimal Design of Water Distribution Systems

This is the final version of the article. Available from American Geophysical Union via the DOI in this record.The first author would like to appreciate the financial support given by both the University of Exeter and the China Scholarship Council (CSC) toward the PhD research. We also appreciate the three anonymous reviewers, who help improve the quality of this paper substantially. The source code of the latest versions of NSGA-II and ε-MOEA can be downloaded from the official website of Kanpur Genetic Algorithms Laboratory via http://www.iitk.ac.in/kangal/codes.shtml. The description of each benchmark problem used in this paper, including the input file of EPANET and the associated best-known Pareto front, can be accessed from the following link to the Centre for Water Systems (http://tinyurl.com/cwsbenchmarks/). GALAXY can be accessed via http://tinyurl.com/cws-galaxy

Runtime analysis of evolutionary multi-objective algorithms optimising the degree and diameter of spanning trees

Motivated by the telecommunication network design, we study the problem of finding diverse set of minimum spanning trees of a certain complete graph based on the two features which are maximum degree and diameter. In this study, we examine a simple multi-objective EA, GSEMO, in solving the two problems where we maximise or minimise the two features at the same time.With a rigorous runtime analysis, we provide understanding of how GSEMO optimize the set of minimum spanning trees in these two different feature spaces.Wanru Gao, Mojgan Pourhassan, Vahid Roostapour, and Frank Neuman

Comparison of Three Commercially Available Dengue NS1 Antigen Capture Assays for Acute Diagnosis of Dengue in Brazil

Dengue is the one of the most prevalent arthropod-borne viral diseases in tropical regions of the world. Manifestations may vary from asymptomatic to potentially fatal complications. Laboratorial diagnosis is essential to diagnose dengue and differentiate it from other diseases. Dengue virus non-structural protein 1 (NS1) may be used as a marker of acute dengue virus infection. Our results, based in the comparison of three NS1 antigen capture assays available, have shown that this approach is reliable for the early diagnosis of dengue infections, especially in the first four days after the onset of the symptoms. A lower sensitivity was observed in DENV-3 cases. Serum positive by virus isolation were more often detected than those positive by RT-PCR by all three assays. Only the Platelia™ NS1 test showed a higher sensitivity in confirming primary infections than secondary ones. In conclusion, NS1 antigen capture commercial kits are useful for diagnosis of acute primary and secondary dengue infections and, in endemic countries where secondary infections are expected to occur, may be used in combination with MAC-ELISA to increase the overall sensitivity of both tests

Comparison of the diagnostic accuracy of commercial NS1-based diagnostic tests for early dengue infection

<p>Abstract</p> <p>Background</p> <p>We compared the diagnostic accuracy and reproducibility of commercially available NS1-based dengue tests and explored factors influencing their sensitivities.</p> <p>Methods</p> <p>Paired analysis of 310 samples previously characterized as positive (n = 218) and negative (n = 92) for viral isolation and/or RT-PCR and/or IgM seroconversion. Masked samples were tested by two observers with Platelia™ Dengue NS1 Ag, second generation Pan-E™ Dengue Early ELISA, SD Dengue NS1 Ag ELISA, Dengue NS1 Ag STRIP™, and SD BIOLINE™ Dengue Duo (NS1/IgM/IgG).</p> <p>Results</p> <p>SD BIOLINE™ NS1/IgM/IgG had the highest sensitivity (80.7% 95%CI 75-85.7) with likelihood ratios of 7.4 (95%CI 4.1-13.8) and 0.21 (95%CI 0.16-0.28). The ELISA-format tests showed comparable sensitivities; all below 75%. STRIP™ and SD NS1 had even lower sensitivities (<65%). The sensitivities significantly decreased in samples taken after 3 days of fever onset, in secondary infections, viral serotypes 2 and 4, and severe dengue. Adding IgM or IgG to SD NS1 increased its sensitivity in all these situations.</p> <p>Conclusions</p> <p>The simultaneous detection of NS1/IgM/IgG would be potentially useful for dengue diagnosis in both endemic and non endemic areas. A negative result does not rule out dengue. Further studies are required to assess the performance and impact of early laboratory diagnosis of dengue in the routine clinical setting.</p