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Discovering SNP Interactions Associated with Breast Cancer Using evolutionary algorithms

Procedia Computer Science 2016年 102卷 562-569页

作者： Fahimeh Mostofi Fahreddin Sadikoglu Department of Computer Engineering Near East University P.O.Box:99138 NicosiaNorth Cyprus Mersin 10 Turkey Department of Electrical and Electronic Engineering Near East University P.O.Box:99138 Nicosia North Cyprus Mersin10 Turkey

Genetic association is a challenging task for the identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases. To fully execute genetic studies of complex diseases, modern geneticists face the challenge of detecting interactions between loci. In this paper, two evolutionary methods were compared to detect associations of single nucleotide polymorphisms (SNPs): a genetic algorithm and Gauss particle swarm optimization. Genetic algorithm was developed with partial matched crossover operator and two different strategies for initialization: regular initialization and top-5 strategy initialization. In both methods for different SNP barcodes (SNP combinations with their corresponding genotypes) the difference between case and control data is computed systematically. The algorithms look for the best combination which is the barcode with maximum difference between the two groups. Analysis results support that the genetic algorithm with top-5 strategy for initialization provides higher frequency difference values than the Gauss particle swarm optimization. It is also proved that a genetic algorithm reduces a computational cost for obtaining higher frequency difference between the case and control group.

关键词： Bioinformatics Breast Cancer evolutionary algorithms Genetic Algorithm Particle Swarm Optimization.

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Optimizing Ethanol Enhanced NAPL Remediation Using evolutionary algorithms

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Procedia Engineering 2016年 162卷 317-323页

作者： Zoi Dokou George P. Karatzas Derya Aydin Sarikurt Nadim K. Copty School of Environmental Engineering Technical University of Crete University Campus Chania 73100 Greece Institute of Environmental Sciences Bogazici University Hisar Campus Bebek Istanbul 34342 Turkey

In the past decades, subsurface non-aqueous phase liquid (NAPL) contamination has been recognized as one of the most widespread and challenging environmental problems. Thus, researchers have focused their efforts on developing and testing the efficiency of remediation methodologies, able to address the unique nature of these contaminants. Recently, in-situ flooding techniques for the accelerated removal of NAPLs trapped in the subsurface have been proposed, where additives are injected together with water upgradient of the NAPL-contaminated area in order to alter the physio-chemical properties of the contaminants, such as interfacial tension, and enhance their solubilities. In this work, the efficiency of ethanol enhanced NAPL remediation is addressed. To this end, a non-linear, multi-objective optimization strategy is developed by combining a multiphase flow simulation model with evolutionary algorithms. Two conflicting optimization objectives are considered: minimizing operation cost and maximizing remediation efficiency, while preventing uncontrolled NAPL mobilization. More specifically, the first objective involves the operation cost of the procedure, which is directly proportional to the pumping rate, duration and ethanol volume used. The second represents the environmental considerations of the problem that, in this work, are described by the maximization of free product removal and the prevention of DNAPL vertical spreading.

关键词： NAPLs cosolvent flushing groundwater remediation evolutionary algorithms particle swarm optimization

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Research on the Mechanical Automation Technology based on evolutionary algorithms and Artifi cial Intelligence Theory

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International Journal of Technology Management 2016年第7期 51-53页

作者： Mindi Duan Qingdao Binhai University in the City of Qingdao Shandong Province 266555 china

In this paper, we conduct research on the mechanical automation technology based on the evolutionary algorithms and artifi cialintelligence theory. Intelligent control theory after 30 years of development has made gratifying achievements. But intelligent control has notyet formed a complete and systematic theory, based on the analysis, design, and there are many important problems in the practical *** information processing is the use of some of the experience and knowledge of information, and the combination of that upper andlower knowledge information processing method. It is expected to solve the problem of insufficient information of pathology, computationcomplexity and the problem of real-time requirements, using the mathematical model is diffi cult to describe the nonlinear problem, etc. Underthis basis, this paper proposes the new mechanical automation technology based on the evolutionary algorithms and artifi cial intelligence theoryto propose the new perspective of dealing with the related challenges.

关键词： Mechanical Automation evolutionary algorithms Artifi cial Intelligence Theory.

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evolutionary algorithms for protein structure modeling 15

Evolutionary algorithms for protein structure modeling

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17th Genetic and evolutionary Computation Conference, GECCO 2015

作者： Shehu, Amarda De Jong, Kenneth A. Department of Computer Science George Mason University FairfaxVA United States

ISBN: (纸本)9781450334884

In the last two decades, great progress has been made in molecular modeling through computational treatments of biological molecules grounded in evolutionary search techniques. evolutionary algorithms (EAs) are gaining popularity beyond exploring the relationship between sequence and function in biomolecules. In particular, recent work is showing the promise of EAs in exploring structure spaces of protein chains to address open problems in computational structural biology, such as de novo structure prediction and other structure modeling problems. Exploring effective interleaving of global and local search has led to hybrid EAs that are now competitive with the Monte Carlo-based frameworks that have traditionally dominated de novo structure prediction. Deeper understanding of the constraints posed by highly-coupled modular systems like proteins and integration of domain knowledge have resulted in effective reproductive operators. Multi-objective optimization has also shown promise in dealing with the conflicting terms that make up protein energy functions and effectively exploring protein energy surfaces. Combinations of these techniques have recently resulted in powerful stochastic search frameworks that go beyond de novo structure prediction and are capable of yielding comprehensive energy landscapes containing possible diverse functionally-relevant structures of proteins. The objective of this tutorial is to introduce the EC community to the rapid developments on EA-based frameworks for protein structure modeling through a concise but comprehensive review of developments in this direction over the last decade. The review will be accompanied with specific detailed highlights and interactive software demonstrations of representative methods. Building on the success and feedback of a related tutorial presented by the organizers at GECCO 2014, highlights will focus on de novo structure prediction and then energy landscape mapping of wildtype and disease-causing varia

关键词： evolutionary algorithms

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Inferring causal networks using fuzzy cognitive maps and evolutionary algorithms with application to gene regulatory network reconstruction

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APPLIED SOFT COMPUTING 2015年 37卷 667-679页

作者： Chen, Ye Mazlack, Lawrence J. Minai, Ali A. Lu, Long J. Cincinnati Childrens Hosp Res Fdn Div Biomed Informat Cincinnati OH 45229 USA Univ Cincinnati Sch Elect & Comp Syst Cincinnati OH 45221 USA Univ Cincinnati Sch Comp Sci & Informat Cincinnati OH 45221 USA Univ Cincinnati Dept Environm Hlth Coll Med Cincinnati OH 45267 USA

Fuzzy cognitive maps have been widely used as abstract models for complex networks. Traditional ways to construct fuzzy cognitive maps rely on domain knowledge. In this paper, we propose to use fuzzy cognitive map learning algorithms to discover domain knowledge in the form of causal networks from data. More specifically, we propose to infer gene regulatory networks from gene expression data. Furthermore, a new efficient fuzzy cognitive map learning algorithm based on a decomposed genetic algorithm is developed to learn large scale networks. In the proposed algorithm, the simulation error is used as the objective function, while the model error is expected to be minimized. Experiments are performed to explore the feasibility of this approach. The high accuracy of the generated models and the approximate correlation between simulation errors and model errors suggest that it is possible to discover causal networks using fuzzy cognitive map learning. We also compared the proposed algorithm with ant colony optimization, differential evolution, and particle swarm optimization in a decomposed framework. Comparison results reveal the advantage of the decomposed genetic algorithm on datasets with small data volumes, large network scales, or the presence of noise. (C) 2015 Elsevier B.V. All rights reserved.

关键词： evolutionary algorithms Fuzzy cognitive map Gene regulatory networks Learning algorithm Optimization

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Tuning a digital multivariable controller for a lab-scale helicopter system via simulated annealing and evolutionary algorithms

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2015年第10期37卷 1254-1273页

作者： Moness, Mohammed Moustafa, Ahmed M. Menia Univ Comp & Syst Engn Dept Fac Engn Al Minya 61111 Egypt

Helicopter systems are considered a complex and challenging control problem due to strong couplings and high non-linearities. In this paper, simulated annealing (SA), as one of the leading methods in search and optimization, is applied to tune a multivariable controller of a lab-scale helicopter system. The lab-scale helicopter system is a multivariable experimental aerodynamic test rig that resembles the behaviour of a real helicopter. The control objectives are quickly to reach a desired position or track a trajectory. A centralized cross-coupled PID controller is used to achieve these objectives. First, SA optimizations are carried out with 24 different initial configurations. Then, the best results of these SA configurations are compared with other controllers obtained with evolutionary algorithms (EAs) of genetic algorithms (GAs), modified particle swarm optimization (MPSO) and differential evolution (DE). The comparisons are based on statistical measures of 20 independent trials, non-linear computer simulations of different input signals and real-time measurements for various commands of positions or trajectories. Results show that SA obtained the best performance index and acceptable time-domain performance on reaching hovering point, following a step command and tracking a sine trajectory compared with the investigated EAs.

关键词： Digital PID control evolutionary algorithms lab-scale helicopters simulated annealing twin rotor

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Topology and shape optimization methods using evolutionary algorithms: a review

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2015年第3期52卷 613-631页

作者： Munk, David J. Vio, Gareth A. Steven, Grant P. Univ Sydney AMME Sydney NSW 2006 Australia

Topology optimization has evolved rapidly since the late 1980s. The optimization of the geometry and topology of structures has a great impact on its performance, and the last two decades have seen an exponential increase in publications on structural optimization. This has mainly been due to the success of material distribution methods, originating in 1988, for generating optimal topologies of structural elements. Previous methods suffered from mathematical complexity and a limited scope for applicability, however with the advent of increased computational power and new techniques topology optimization has grown into a design tool used by industry. There are two main fields in structural topology optimization, gradient based, where mathematical models are derived to calculate the sensitivities of the design variables, and non gradient based, where material is removed or included using a sensitivity function. Both fields have been researched in great detail over the last two decades, to the point where structural topology optimization has been applied to real world structures. It is the objective of this review paper to present an overview of the developments in non gradient based structural topology and shape optimization, with a focus on evolutionary algorithms, which began as a non gradient method, but have developed to incorporate gradient based techniques. Starting with the early work and development of the popular algorithms and focusing on the various applications. The sensitivity functions for various optimization tasks are presented and real world applications are analyzed. The article concludes with new applications of topology optimization and applications in various engineering fields.

关键词： Structural optimisation Non gradient evolutionary algorithms Topology optoimisation Shape optimisation Applications

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Optimization of thin noise barrier designs using evolutionary algorithms and a Dual BEM Formulation

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JOURNAL OF SOUND AND VIBRATION 2015年 334卷 219-238页

作者： Toledo, R. Aznarez, J. J. Maeso, O. Greiner, D. Univ Las Palmas Gran Canaria Inst Sistemas Inteligentes & Aplicac Numer SIANI Las Palmas Gran Canaria 35017 Spain

This work aims at assessing the acoustic efficiency of different thin noise barrier models. These designs frequently feature complex profiles and their implementation in shape optimization processes may not always be easy in terms of determining their topological feasibility. A methodology to conduct both overall shape and top edge optimizations of thin cross section acoustic barriers by idealizing them as profiles with null boundary thickness is proposed. This procedure is based on the maximization of the insertion loss of candidate profiles proposed by an evolutionary algorithm. The special nature of these sorts of barriers makes necessary the implementation of a complementary formulation to the classical Boundary Element Method (BEM). Numerical simulations of the barriers' performance are conducted by using a 2D Dual BEM code in eight different barrier configurations (covering overall shaped and top edge configurations;spline curved and polynomial shaped based designs;rigid and noise absorbing boundaries materials). While results are achieved by using a specific receivers' scheme, the influence of the receivers' location on the acoustic performance is previously addressed. With the purpose of testing the methodology here presented, a numerical model validation on the basis of experimental results from a scale model test [34] is conducted. Results obtained show the usefulness of representing complex thin barrier configurations as null boundary thickness-like models. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： NOISE barriers evolutionary algorithms BOUNDARY element methods MATHEMATICAL optimization CROSS-sectional method PERFORMANCE evaluation SIMULATION methods & models

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Design of an autonomous intelligent Demand-Side Management system using stochastic optimisation evolutionary algorithms

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NEUROCOMPUTING 2015年 170卷 270-285页

作者： Galvan-Lopez, Edgar Curran, Tom McDermott, James Carroll, Paula Univ Paris 11 INRIA Saclay & LRI TAO Project Orsay France Univ Dublin Trinity Coll Sch Comp Sci & Stat Dublin 2 Ireland Univ Coll Dublin Lochlann Quinn Sch Business Management Informat Syst Dublin Ireland

Demand-Side Management systems aim to modulate energy consumption at the customer side of the meter using price incentives. Current incentive schemes allow consumers to reduce their costs, and from the point of view of the supplier play a role in load balancing, but do not lead to optimal demand patterns. In the context of charging fleets of electric vehicles, we propose a centralised method for setting overnight charging schedules. This method uses evolutionary algorithms to automatically search for optimal plans, representing both the charging schedule and the energy drawn from the grid at each time-step. In successive experiments, we optimise for increased state of charge, reduced peak demand, and reduced consumer costs. In simulations, the centralised method achieves improvements in performance relative to simple models of non-centralised consumer behaviour. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Demand-Side Management systems evolutionary algorithms Electric vehicles Peak-to-average ratio Electricity costs Smart grid time-of-use pricing

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Optimal siting and sizing of UPFC using evolutionary algorithms

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 69卷 222-231页

作者： Alamelu, Somasundaram Baskar, S. Babulal, C. K. Jeyadevi, S. Loyola ICAM Coll Engn & Technol Dept Elect & Elect Engn Madras Tamil Nadu India Thiagarajar Coll Engn Dept Elect & Elect Engn Virudunagar Tamil Nadu India Kamaraj Coll Engn & Technol Dept Elect & Elect Engn Virudunagar Tamil Nadu India

This paper addresses an application of evolutionary algorithms to optimal siting and sizing of UPFC which are formulated as single and multiobjective optimization problems. The decision variables such as optimal location, both line and distance of UPFC from the sending end, control parameters of UPFC and system reactive power reserves are considered in the optimization process. Minimization of total costs including installation cost of UPFC and enhancement of the loadability limit are considered as objectives. To reduce the complexity in modeling and the number of variables and constraints, transformer model of UPFC is used for simulation purposes. CMAES and NSGA-II algorithms are used for optimal siting and sizing of UPFC on IEEE 14 and 30 bus test systems. NSGA-II algorithm is tested on IEEE 118 bus system to prove the versatility of the algorithm when applied to large systems. To validate the results of transformer model of UPFC for optimal siting and sizing, results using other models are considered. In single objective optimization problem, CMAES algorithm with transformer model yields better results when compared to other UPFC models. The statistical results conducted on 20 independent trials of CMAES algorithm authenticate the results obtained. For validating the results of NSGA-II with transformer model for optimal siting and sizing of UPFC, the reference Pareto front generated using multiple run CMAES algorithm by minimizing weighted objective is considered. In multiobjective optimization problem, the similarity between the generated Pareto front and the reference Pareto front validates the results obtained. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Optimal location Transformer model of UPFC evolutionary algorithms PDIP Sensitivity analysis Maximum loadability

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