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Abstract

This paper presents the current stage of the development of EA-MOSGWA – a tool for identifying causal genes in Genome Wide Association Studies (GWAS). The main goal of GWAS is to identify chromosomal regions which are associated with a particular disease (e.g. diabetes, cancer) or with some quantitative trait (e.g height or blood pressure). To this end hundreds of thousands of Single Nucleotide Polymorphisms (SNP) are genotyped. One is then interested to identify as many SNPs as possible which are associated with the trait in question, while at the same time minimizing the number of false detections.

The software package MOSGWA allows to detect SNPs via variable selection using the criterion mBIC2, a modified version of the Schwarz Bayesian Information Criterion. MOSGWA tries to minimize mBIC2 using some stepwise selection methods, whereas EA-MOSGWA applies some advanced evolutionary algorithms to achieve the same goal. We present results from an extensive simulation study where we compare the performance of EA-MOSGWA when using different parameter settings. We also consider using a clustering procedure to relax the multiple testing correction in mBIC2. Finally we compare results from EA-MOSGWA with the original stepwise search from MOSGWA, and show that the newly proposed algorithm has good properties in terms of minimizing the mBIC2 criterion, as well as in minimizing the misclassification rate of detected SNPs.

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Authors and Affiliations

Artur Gola
Małgorzata Bogdan
Florian Frommlet
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Abstract

Evolutionary computing and algorithms are well known tools of optimisation that are utilized for various areas of analogue electronic circuits design and diagnosis. This paper presents the possibility of using two evolutionary algorithms - genetic algorithm and evolutionary strategies - for the purpose of analogue circuits yield and cost optimisation. Terms: technologic and parametric yield are defined. Procedures of parametric yield optimisation, such as a design centring, a design tolerancing, a design centring with tolerancing, are introduced. Basics of genetic algorithm and evolutionary strategies are presented, differences between these two algorithms are highlighted, certain aspects of implementation are discussed. Effectiveness of both algorithms in parametric yield optimisation has been tested on several examples and results have been presented. A share of evolutionary algorithms computation cost in a total optimisation cost is analyzed.

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Authors and Affiliations

P. Jantos
J. Rutkowski
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Abstract

An automated procedure based on evolutionary computation and Finite Element Analysis (FEA) is proposed to synthesize the optimal distribution of nanoparticles (NPs) in multi-site injection for a Magnetic Fluid Hyperthermia (MFH) therapy. Evolution Strategy and Non dominated Sorting Genetic Algorithm (NSGA) are used as optimization procedures coupled with a Finite Element computation tool.

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Authors and Affiliations

Paolo Di Barba
Fabrizio Dughiero
Elisabetta Sieni
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Abstract

The presented paper concerns CFD optimization of the straight-through labyrinth seal with a smooth land. The aim of the process was to reduce the leakage flow through a labyrinth seal with two fins. Due to the complexity of the problem and for the sake of the computation time, a decision was made to modify the standard evolutionary optimization algorithm by adding an approach based on a metamodel. Five basic geometrical parameters of the labyrinth seal were taken into account: the angles of the seal’s two fins, and the fin width, height and pitch. Other parameters were constrained, including the clearance over the fins. The CFD calculations were carried out using the ANSYS-CFX commercial code. The in-house optimization algorithm was prepared in the Matlab environment. The presented metamodel was built using a Multi-Layer Perceptron Neural Network which was trained using the Levenberg-Marquardt algorithm. The Neural Network training and validation were carried out based on the data from the CFD analysis performed for different geometrical configurations of the labyrinth seal. The initial response surface was built based on the design of the experiment (DOE). The novelty of the proposed methodology is the steady improvement in the response surface goodness of fit. The accuracy of the response surface is increased by CFD calculations of the labyrinth seal additional geometrical configurations. These configurations are created based on the evolutionary algorithm operators such as selection, crossover and mutation. The created metamodel makes it possible to run a fast optimization process using a previously prepared response surface. The metamodel solution is validated against CFD calculations. It then complements the next generation of the evolutionary algorithm.

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Bibliography

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Authors and Affiliations

Sebastian Rulik
1
Włodzimierz Wróblewski
1
Daniel Frączek
1

  1. Silesian University of Technology, Institute of Power Engineering and Technology, Gliwice, Poland
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Abstract

This article provides an optimized solution to the problem of passive shielding against static magnetic fields with any number of spherical shells. It is known, that the shielding factor of a layered structure increases in contrast to a single shell with the same overall thickness. For the reduction of weight and cost by given material parameters and available space the best system for the layer positions has to be found. Because classic magnetically shielded rooms are very heavy, this system will be used to develop a transportable Zero-Gauss-Chamber. To handle this problem, a new way was developed, in which for the first time the solution with regard to shielding and weight was optimized. Therefore, a solution for the most general case of spherical shells was chosen with an adapted boundary condition. This solution was expanded to an arbitrary number of layers and permeabilities. With this analytic solution a differential evolution algorithm is able to find the best partition of the shells. These optimized solutions are verified by numerical solutions made by the Finite Element Method (FEM). After that the solutions of different raw data are determined and investigated.
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Bibliography

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Authors and Affiliations

Patrick Alexander Ralf
1
ORCID: ORCID
Christian Kreischer
1

  1. Helmut Schmidt University, University of the Federal Armed Forced Hamburg, Germany

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