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Theoretical evaluation of the parameters of glucose metabolism on the basis of continuous glycemia monitoring data using mathematical modeling

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Biophysics (Russian Federation) 2017年第5期62卷 842-847页

作者： Sveshnikova, A.N. Panteleev, M.A. Dreval, A.V. Shestakova, T.P. Medvedev, O.S. Dreval, O.A. Department of Physics Lomonosov Moscow State University Moscow 119991 Russian Federation Federal Research and Clinical Center of Pediatric Hematology Oncology and Immunology named after D. Rogachev Moscow 117197 Russian Federation Moscow Regional Research and Clinical Institute named after M.F. Vladimirskii Moscow 129110 Russian Federation Faculty of Basic Medicine Moscow State University Moscow 119192 Russian Federation

The aim of this paper is to construct a mathematical model that takes the main physiological parameters of blood-glucose regulation into account, in order to identify these parameters for an individual patient according to continuous glucose-monitoring data. The constructed mathematical model consists of six ordinary differential equations that describe the dynamics of changes in glucose concentrations, as well as insulin and anti-insulin factors in the blood. Estimation of the parameters of the equations was performed using an evolutionary programming method. The model predictions were fitted to the continuous glucosemonitoring data. As a result of the identification of the model parameters for two patients with type 1 diabetes mellitus, the estimated insulin secretion was close to zero and the estimated glucose utilization and insulin clearance were increased in comparison with the data for healthy donors. Here, we present a personalized model of the regulation of blood glucose, which can be used to predict the results of continuous glucose monitoring depending on modification of the prescribed glucose-lowering therapy. This approach can significantly reduce the number of iterations of the selection of medical hypoglycemic therapy and therefore increase the effectiveness of treatment according to glucose-monitoring data. © 2017, Pleiades Publishing, Inc.

关键词： continuous glucose monitoring evolutionary programming mathematical modeling pharmacological modeling type 1 diabetes

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A TEP-Based Approach for Optimal Thrust Direction of Lunar Soft Landing 1

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International Conference on Artificial Intelligence and evolutionary Computations in Engineering Systems (ICAIECES)

作者： Pragallapati, Naveen Narasimham, N. V. S. L. G Narayanamma Inst Technol & Sci Dept Humanities & Math Hyderabad 500104 Andhra Pradesh India

ISBN: (数字)9789811031748

ISBN: (纸本)9789811031748;9789811031731

Determination of optimal thrust direction or steering angle for lunar soft landing trajectory is attempted in this article. The problem is complex due to the presence of system constraints and local minima. An exhaustive search of optimal thrust direction incurs high computational costs. The problem was solved as an optimum initial value estimation problem. Taboo evolutionary programming (TEP) is utilized to compute the optimal estimates. The study gives the integration of TEP technique in solving the governing nonlinear differential equations where a control parameter involved. The results are compared with available results in literature and it shows that the solution based on TEP algorithm is comparable to the counterpart. Further, sensitivity of design parameters such as terminal altitude, true anomaly, and terminal velocity over the final landing mass at the touch down is also examined.

关键词： evolutionary programming Soft landing Trajectory optimization Parameter control

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Multiple strategy generation for war gaming

Multiple strategy generation for war gaming

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Conference on Enabling Technologies for Simulation Science VIII

作者： Revello, T McCartney, R Santos, E Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA

ISBN: (纸本)0819453463

In this paper we present a framework for the automated generation of strategies that accounts for the multiple kinds of uncertainty found in war games, provides for a domain independent approach to strategy generation, and results in robust strategies. Our approach is to sample over multiple trials for varying victory conditions, different threat profiles, and variable system performance to achieve a degree of independence in the resulting strategy. This allows a search for robust strategies versus those that are effective only under specific conditions. War games have uncertainty in what is needed to achieve victory, in system performance, and in threat behavior. There are multiple options for forces, employment, and warfare styles. All these factors combine to produce a large, complex space of possible solutions or strategies. Through the use of powerful search techniques like evolutionary computation and modem computing assets it has become practical to search this space for strategies with robust performance. Our framework is modular in nature, allowing a variety of search techniques, warfare scenarios, system models, and other parameters to be interchanged. In the paper the framework described above is demonstrated using an antisubmarine warfare scenario. evolutionary programming techniques are used to search the space of possible strategies.

关键词： war gaming strategy generation multiple strategies evaluation evolutionary computing evolutionary programming adversary modeling

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Study on new evolutionary neural network

Study on new evolutionary neural network

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International Conference on Machine Learning and Cybernetics

作者： Gao, W Wuhan Polytech Univ Wuhan 430023 Peoples R China

ISBN: (纸本)0780378652

The evolutionary neural network model can be generated combining the evolutionary computation with neural network. Based on analysis of merits and demerits of previously proposed evolutionary neural network models, and combining the immunized evolutionary programming proposed by the author, BP neural network, a new evolutionary neural network model whose architecture and connection weights evolve simultaneously is proposed. At last, through the typical XOR problem, the new model is compared and analyzed with BP neural network and traditional evolutionary neural network. The computing results show that the precision and efficiency of the new model are all good.

关键词： evolutionary neural network evolutionary programming genetic algorithm immunized evolutionary programming

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A Chatterbot Based on Genetic Algorithm: Preliminary Results 4th

A Chatterbot Based on Genetic Algorithm: Preliminary Results

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4th International Conference on Applied Informatics (ICAI)

作者： Orellana, Cristian Tobar, Martin Yazan, Jeremy Peluffo-Ordonez, D. Guachi-Guachi, Lorena Univ Int Ecuador Dept Mechatron Av Simon Bolivar Quito 170411 Ecuador Mohammed VI Polytech Univ Ben Guerir Morocco SDAS Res Grp Ibarra Ecuador Corp Univ Autonoma Narino Fac Engn Dept Elect Pasto 520001 Colombia

ISBN: (纸本)9783030896546;9783030896539

Chatterbots are programs that simulate an intelligent conversation with people. They are commonly used in customer service, product suggestions, e-commerce, travel and vacations, queries, and complaints. Although some works have presented valuable studies by using several technologies including evolutionary computing, artificial intelligence, machine learning, and natural language processing, creating chatterbots with a low rate of grammatical errors and good user satisfaction is still a challenging task. Therefore, this work introduces a preliminary study for the development of a GA-based chatterbot that generates intelligent dialogues with a low rate of grammatical errors and a strong sense of responsiveness, so boosting the personals satisfaction of individuals who interact with it. Preliminary results show that the proposed GA-based chatterbot yields 69% of "Good" responses for typical conversations regarding orders and receipts in a cafeteria.

关键词： Chatbot evolutionary programming Natural language processing

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A New Maximum Weight Submatrix Solver Tool for Identifying Mutated Driver Pathways in Cancer using Python 10

A New Maximum Weight Submatrix Solver Tool for Identifying M...

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10th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Kharroubi, Fouad El Kati, Yassine Wang, Shu-Lin El Jourmi, Mohammed Ouahmane, Hassan Chouaib Doukkali Univ Natl Sch Appl Sci ENSAJ Networks & Comp Sci LTI Lab Dept Telecommun El Jadida 24002 Morocco Hunan Univ Sch Comp Sci & Elect Engn Changsha 41182 Peoples R China

ISBN: (纸本)9781728137834

Cancer may be a title given to all kind of infections caused by hereditary changes, such as substantial changes in DNA. Cancers are caused by mutations-which occur during cell division- that may possibly be inherited, provoked by environmental determinants, or result from some random DNA replication errors which simply maybe considered as bad luck. Recently in bioinformatics, one of the most heated topics is to search about the separation between driver mutations that lead to cancer and passenger mutations, which are well noted to be neutral and do not have any part in this disease development. Indeed, to solve this problem, several approaches and techniques were used in the literature. The main aim of this work is to show a new computation tool called "The Maximum Weight Submatrix Solver (MWS Solver)" using Python language. In fact, we programmed three metaheuristics called: Genetic Algorithm, evolutionary programming and Random Optimization Algorithm, as an approximate approach in order to maximize the weight and sample all the possible driver pathways that can be found. A comparison of these approximate methods with other previous exact and random methods has also been taken into consideration. The experimental tests demonstrate that the proposed algorithms implemented via MWS Solver may become a useful open source and free complementary instrument for finding cancer pathways.

关键词： Maximum Weight Submatrix Problem Driver Pathways Driver Mutation Genetic Algorithm Random Algorithm evolutionary programming

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New heuristic-based design of robust power system stabilizers

New heuristic-based design of robust power system stabilizer...

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International Conference on Power System Technology (POWERCON 2004)

作者： Dechanupaprittha, S Ngamroo, I Pasupa, K Tippayachai, J Hongesombut, K Mitani, Y Thammasat Univ SIIT Elect Power Engn Program Pathum Thani 12121 Thailand

ISBN: (纸本)0780386108

This paper proposes a new robust design of power system stabilizers (PSSs) in a multimachine power system using a heuristic optimization method. The structure of each PSS used is similar to that of a conventional lead/lag stabilizer. The proposed design regards a multimachine power system with PSSs as a multi-input multi-output (MIMO) control system. Additionally, a multiplicative uncertainty model is taken into account in the power system representation. Accordingly, the robust stability margin can be guaranteed by a multiplicative stability margin (MSM). The presented method utilizes the MSM as the design specification for robust stability. To acquire the control parameters of PSSs, a control design in MIMO system is formulated as an optimization problem. In the selection of objective function, not only disturbance attenuation performance but also robust stability indices are considered. Subsequently, the hybrid tabu search and evolutionary programming (Hybrid TS/EP) is employed to search for the optimal parameters. The significant effects of designed PSSs are investigated under several system operating conditions.

关键词： robust control tabu search evolutionary programming optimization problems multivariable control system power system stabilizer

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Constrained Reactive Power Control Using evolutionary Computation Technique for Static Security Enhancement

Constrained Reactive Power Control Using Evolutionary Comput...

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2nd International Conference on Computer and Electrical Engineering

作者： Abdullah, Nor Rul Hasma Musirin, Ismail Murthada, Muhammad Univ Malaysia Pahang Fac Elect & Elect Engn Kuantan Pahang Malaysia Univ Teknol MARA Fac Elect Engn Shah Alam Malaysia

ISBN: (纸本)9780769539256

This paper presents evolutionary Computing technique for solving constrained reactive power control (CRPC) problem in the attempt to enhance voltage stability under contingencies, while minimizing transmission loss and maintaining voltage level at an acceptable level. In this study, evolutionary programming (EP) was chosen as the evolutionary Computing (EC) technique for solving the CRPC;taking into consideration two separate objective functions. Static voltage stability enhancement and minimization of real power loss are implemented separately on a reliability test system. Comparative studies performed with respect to Artificial Immune System (AIS) have highlighted that EP outperformed AIS for both objective functions.

关键词： evolutionary Computing evolutionary programming Artificial Immune System Constrained Reactive Power Control Generator Outage Line Outage Static Voltage Stability Index

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A Study of Direct and Indirect Encoding in Phenotype-Genotype Relationships 20th

A Study of Direct and Indirect Encoding in Phenotype-Genotyp...

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20th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Meli, Clyde Nezval, Vitezslav Oplatkova, Zuzana Kominkova Buttigieg, Victor Staines, Anthony Spiteri Univ Malta Dept Comp Informat Syst Msida Malta Tomas Bata Univ Zlin Fac Appl Informat Dept Informat & Artificial Intelligence Zlin Czech Republic Univ Malta Dept Commun & Comp Engn Msida Malta

ISBN: (纸本)9783030878979;9783030878962

This paper examines phenotype and genotype mappings that are biologically inspired. These types of coding are used in evolutionary computation. Direct and indirect encoding are studied. The determination of genotype and phenotype relationships and the connection to genetic algorithms, evolutionary programming and biology are examined in the light of newer advances. The NEAT and HyperNEAT algorithms are applied to the 2D Walker [41] problem of an agent learning how to walk. Results and findings are discussed, and conclusions are given. Indirect coding did not improve the situation. This paper shows that indirect coding is not useful in every situation.

关键词： Indirect encoding Direct coding Genotype Phenotype Genetic algorithms evolutionary programming Neuroevolution NEAT HyperNEAT

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Distribution Network Reconfiguration for Voltage Stability Enhancement via Feasibility-Preserving evolutionary Optimization

Distribution Network Reconfiguration for Voltage Stability E...

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IEEE Electrical Power and Energy Conference (EPEC)

作者： Rojas, Alberto Landeros Koziel, Slawomir Abdel-Fattah, Mohamed F. Gutierrez-Alcaraz, Guillermo Reykjavik Univ Engn Optimizat & Modeling Ctr Reykjavik Iceland PGIIE Inst Tecnol Morelia Dept Elect Engn Morelia Michoacan Mexico

ISBN: (纸本)9781538654194

This paper addresses a problem of power distribution network optimization, specifically, an improvement of the voltage stability index (VSI). A customized algorithm, referred to as feasibility preserving evolutionary optimization (FPEO), is developed and employed that involves recombination and mutation operators tailored to maintain the radial architecture of the network and enhances VSI through network reconfiguration. The algorithm performance is validated using the three standard test distribution systems, 33-, 69-, and 119-bus networks. Excellent repeatability of results is achieved as demonstrated through comprehensive statistical analysis. The optimal reconfiguration found by the proposed algorithm significantly improves the VSI of the buses most prone to voltage collapse. At the same time, the voltage profile improvement is obtained along with reduction of the power losses.

关键词： Distribution network reconfiguration voltage stability index evolutionary programming feasibility preserving power systems radial networks smart grid

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