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检索条件"主题词=Evolution algorithms"

共 50 条记录，以下是21-30 订阅

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Using Predictive Model of Mean Monthly Flows for Large Open Reservoirs Hydropower Control 16

Using Predictive Model of Mean Monthly Flows for Large Open ...

引用

16th International Conference on Water Distribution System Analysis (WDSA)

作者： Mensik, P. Stary, M. Marton, D. Brno Univ Technol Fac Civil Engn Inst Landscape & Water Management Brno 60200 Czech Republic

Conventional hydropower reservoir operations are mostly based on rules or rule curves. The paper describes algorithm which has been created on idea of adaptive control theory. The adaptive control approach uses repeatedly generated medium-term water flow predictions on a several months ahead as inflows into the large open reservoirs. Values of control outflows are searched by evolution algorithm optimization methods. Principle of the predictive model of average monthly flows is introduced in this paper. The algorithm is applied to the operation hydropower control of selected reservoir. (C) 2014 The Authors. Published by Elsevier Ltd.

关键词： Adaptive evolution algorithms hydropower function large open reservoir operative control optimization predictive model

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evolutionary search techniques application in automated lay-planning optimization problem

Evolutionary search techniques application in automated lay-...

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1995 IEEE International Conference on Systems, Man and Cybernetics - Intelligent Systems for the 21st-Century

作者： BOUNSAYTHIP, C MAOUCHE, S NEUS, M UNIV LILLE 1 CTR AUTOMAT LILLEF-59655 VILLENEUVE DASCQFRANCE

ISBN: (纸本)0780325605

The purpose of this paper fits in optimization of apparel shapes layout. The aim is to mark all the shapes to be cut onto the sheet by minimizing unoccupied spaces. As the problem involves many sub-optimal solutions or many local optima, genetic-like algorithm is used to handle the layout process. The application of genetic algorithm (GA) needs parameter encoding. At first, an individual is encoded by combs coordinates of shape description. Strings are of variable length and genetic operators are created to this domain specific encoding. As genetic algorithm used alone is not very efficient, we made attempt to hybridize GA with simulated annealing (SA). Results provided will be compared with GA alone and with our previous results by tree search.

关键词： evolution algorithms HYBRID GENETIC ALGORITHM APPAREL SHAPES LAYOUT

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A MOEA/D Approach to Exploit the Crucial Structure of Convolution Kernels 10

A MOEA/D Approach to Exploit the Crucial Structure of Convol...

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10th International Conference on Advanced Computational Intelligence (ICACI)

作者： Zhang, Qianyu Li, Bin Zhang, Feng Univ Sci & Technol China Sch Informat Sci & Technol Hefei Anhui Peoples R China

ISBN: (纸本)9781538643624

Deep convolutional neural networks achieved remarkably performance on computer vision tasks. However, they are always over-parameterized and computationally intensive to deploy on computing platforms with limited hardware resources. In this paper, a MOEA/D based pruning approach is proposed. We exploit the crucial structure of convolution kernels using MOEA/D based pruning approach and find 88% computational redundancy in convolutional layers of LeNet-5 without any accuracy loss. Networks are encoded as chromosomes in continues or discrete spaces. A multi-objective evolutionary algorithm, MOEA/D, is employed to evolve multiple convolutional neural networks simultaneously subject to accuracy and computational cost as two conflicting objectives. Several experiments are conducted on three classical image classification tasks. Statistical results demonstrate that our proposed method is both effective and superior to other methods.

关键词： evolution algorithms MOEA/D convolutional neural networks computational complexity

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A Comparison of Population-based Optimization Techniques for Water Distribution System Expansion and Operation 16

A Comparison of Population-based Optimization Techniques for...

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16th International Conference on Water Distribution System Analysis (WDSA)

作者： Morley, M. S. Tricarico, C. Exeter Adv Analyt LLP 109 Penn Rd Exeter EX4 6DT Devon England Univ Cassino & Southern Lazio Dept Civil & Mech Engn I-03043 Cassino Italy

This paper presents a water distribution system expansion and operation methodology employing population-based optimization algorithms applied to the Battle of Background Leakage Assessment for Water Networks competition. The problem is formulated as constrained single and multiple-objective optimization problems and implemented in a generic hydraulic optimization and benchmarking software application (Acquamark). To accelerate the evaluation of potential solutions, a distributed computing approach is employed, where necessary, to permit multiple solutions to be executed and evaluated in parallel. A pressure-driven demand extension to the EPANET hydraulic model is also employed to assist the optimization techniques in accurately ranking near-feasible solutions and to dynamically allocate leakage demand to the end nodes of each pipe. (C) 2014 The Authors. Published by Elsevier Ltd.

关键词： Multiple Objective Optimization evolution algorithms BBLAWN Water Distribution Systems Leakage

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Features Selection Approaches for Intrusion Detection Systems based on evolutionary algorithms

Features Selection Approaches for Intrusion Detection System...

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3rd International Conference on Signals, Circuits and Systems

作者： Zaman, Safaa El-Abd, Mohammed Karray, Falchri Univ Waterloo ECE Dept Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781424443970

Intrusion Detection Systems (IDSs) deal with large amount of data containing irrelevant and redundant features, which leads to slow training and testing processes, heavy computational resources and low detection accuracy. Therefore, the features selection is an important issue in intrusion detection. Reducing the features set improves the system accuracy and speeds up the training and testing phases considerably. In this paper, we investigate the use of evolution algorithms for features selection approach in Intrusion Detection System (IDS). In order to this we have implemented three different evolutionary computation algorithms: binary Genetic algorithms (GAs), binary Particle Swarm Optimization (PSO) and Differential evolution (DE). The performances of three algorithms according to different metrics are compared when applied to the KDD Cup 1999 dataset.

关键词： Intrusion detection Systems features selection evolution algorithms binary Genetic algorithms binary Particle Swarm Optimization Differential evolution

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Hybrid optimization method for strategic control of water withdrawal from water reservoir with using support vector machines 18th

Hybrid optimization method for strategic control of water wi...

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18th International Conference on Water Distribution System Analysis (WDSA)

作者： Mensik, Pavel Marton, Daniel Brno Univ Technol Fac Civil Engn Inst Landscape & Water Management Veveri 95 Brno 60200 Czech Republic

The aim of strategic control is the effort to achieve optimal water resource management (water reservoir). Classic strategic control of water withdrawal from water reservoir are based mainly on the rules and rules curves, which are created by generalization of historical data of water inflows to the reservoirs and water demand. Discharge series are changing in the time due to expected climate change. It is necessary to looking for intelligent water withdrawal control, which will allow to react on these hydrological changes and contribute to the efficient use of accumulated water for ensure water demand. The paper will describe the algorithm based on adaptive control. The normal adaptive control required knowledge of the water flow medium-term prediction into the reservoir. The created algorithm of intelligent water withdrawal control does not require knowledge of hydrological predictions. This control method is based on a suitable combination optimization method with the Support vector machines method. The control algorithm is one of the possible measures to mitigate the negative impacts of droughts and water scarcity. The algorithm of adaptive control is applied to the control of water withdrawal from selected single-reservoir. The results are compared with usual rules for water withdrawal control. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： Adaptive evolution algorithms Operative Control Reservoir Strategic Control Optimization Support Vector Machines Water Demand Water Resource Water Withdrawal

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Solving economic machining models using quantum-pattern search-particle swarm hybrid optimization algorithm

Solving economic machining models using quantum-pattern sear...

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11th IASTED International Conference on Artificial Intelligence and Soft Computing

作者： Alfares, Fawzan S. Alenezi, Hussein Alajmi, Mahdi S. Department of Manufacturing Engineering PAAET Kaifan P.O. Box: 68425 Kuwait School of Engineering and Design Brunel University Uxbridge Middlesex UB8 3PH United Kingdom

ISBN: (纸本)9780889866935

This paper introduce novel hybrid optimization algorithm consists from elements of three optimization algorithms. The first algorithm is the particle swarm optimization, in which the element "velocity" is used. This element provides the distance to jump from the current position to the new position. The second element acquired from the pattern search algorithm in which the movement of the solution candidate in the neighborhood performed. This movement called compass movement where the direction of movement is to the up, down, right, or left. The last element obtained from the quantum-inspired algorithm. This element says that if no best solution found after perforining the compass movement then the solution candidate will move to one of these positions that have the highest probability. The formula of the probabilities based on quantum computing theory. To investigate the performance of the proposed algorithm, machining economic models are used. The results have showed promising application of this hybrid algorithm to the field of mechanical engineering.

关键词： optimization evolution algorithms quantum theory pattern search algorithm and particle swarm optimization

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Algoritmy pro operativní plánování výroby

Algoritmy pro operativní plánování výroby

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作者： Kršák, Pavol Brno University of Technology

Bakalárska práca sa zaoberá riešením problematiky operatívneho plánovania výroby. Úče- lom práce je prezentovať problematiku operatívneho plánovania výroby a... 详细信息

Bakalárska práca sa zaoberá riešením problematiky operatívneho plánovania výroby. Úče- lom práce je prezentovať problematiku operatívneho plánovania výroby a následný ná- vrh a realizácia plánovacích algoritmov v jazyku C# na konkrétny plánovací problém. Pri prezentácii problematiky je uvedená spoločnosť "‘COMPAS automatizace s.r.o."’, ktorá umožnila realizovanie projektu. V práci sú rozobrané plánovacie metódy a spô- soby ich realizácie. Konkrétnejšie sú rozpracované evolučné algoritmy, expertné systémy v plánovaní a deterministické plánovanie cez lineárne programovanie. V tretej kapitole je charakterizovaný plánovací problém výroby kávovej zmesi, ktorý mi bol poskytnutý spoločnosťou COMPAS. Následne je rozpracovaný samotný návrh riešenia založený na evolučnom princípe. Riešenie pozostáva z modelu technológie na výrobu kávovej zmesi a ActionListu. ActionList predstavuje súbor pravidiel, ktorými sa model riadi. Potom sú zadefinované pravidlá podrobené evolučným procesom so snahou nájsť lepšie existujúce riešenia. Realizované funkcie použiteľné v systéme COMES pre opakované využitie pri implementácii modulu COMES Modeller sú rozpracované spolu s podrobným popisom v štvrtej kapitole.

关键词： operatívne plánovanie výroby evolučné algoritmy plánovanie optimalizácia plánovacie algoritmy advance production planning evolution algorithms scheduling optimalization schedu- ling algorithms Text

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Genetické programování s jazykem Brainfuck

Genetické programování s jazykem Brainfuck

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作者： Minařík, Miloš Brno University of Technology

Tato práce se zabývá výzkumem možností použití jazyka brainfuck ke genetickému programování. V teoretické části jsou rozebrány základní mechanismy ... 详细信息

Tato práce se zabývá výzkumem možností použití jazyka brainfuck ke genetickému programování. V teoretické části jsou rozebrány základní mechanismy genetického programování (selekce, křížení, mutace), jejich různé varianty a použití. Praktická část obsahuje kapitoly zabývající se samotnou implementací programového rozhraní pro tuto práci a prováděné experimenty. Výsledky získané vyhodnocením experimentů jsou shrnuty v záverečné části, kde je rovněž nastíněn další možný vývoj v oblasti genetického programování s jazykem brainfuck.

关键词： genetické programování brainfuck evoluční algoritmy genetic programming brainfuck evolution algorithms Text

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Variace evolučního SOMA algoritmu pro dynamické úlohy

Variace evolučního SOMA algoritmu pro dynamické úlohy

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作者： Pokorný, Jan Brno University of Technology

Tato práce se zabývá používáním evolučního algoritmu SOMA a testováním jeho variant zaměřených na řešení dynamických problémů. Na začátku stručně... 详细信息

Tato práce se zabývá používáním evolučního algoritmu SOMA a testováním jeho variant zaměřených na řešení dynamických problémů. Na začátku stručně seznamuje s problematikou evolučních algoritmů a poté se zaměřuje na evoluční algoritmus SOMA. Popisuje potíže, se kterými se potýká, jak pro statické tak i dynamické úlohy. Zmiňuje postupy, které se používají pro jejich odstraňování. Popisuje nejčastěji používané strategie -- All To One, All To Random, All To All a All To All Adaptive a poukazuje na jejich výhody a nedostatky. Dále je navrhnuta i další strategie prohledávání zaměřená na funkce dynamicky se měnící nezávisle na běhu algoritmu. Samostatná kapitola je věnována projektové části práce. Je zde popsán postup implementace a propojování jednotlivých použitých programů. Tato část je dostupná na přiloženém CD spolu s výsledky testování jednotlivých strategií. Tabulky s průměrnými hodnotami jsou také součástí práce.

关键词： SOMA evoluční algoritmy SOMA evolution algorithms Text

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