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Reduction Technique for Instance-based Learning Using Distributed genetic algorithms

INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2011年第3期4卷 47-60页

作者： Al-Ramadin, Tahseen A. Al Hussein Bin Talal Univ Maan 71111 Jordan

This work addresses the problem of instance reduction using a distributed implementation of genetic algorithms. Different existing parallel and distributed models for parallelizing genetic algorithms are investigated and applied here to solve the problem of instance reduction. a new parallel model is proposed and implemented we called Global Control parallel genetic algorithm. The results showed enormous reduction in time of 90% over the other models. The resulted dataset showed an acceptable accuracy results on average over all datasets. The model achieved a better reduction in dataset size of 90.22% compared to the other models that didn't get better that 87.91%. Thus, the proposed distributed system model for instance reduction showed better performance over all model in reducing the time and even reducing the training dataset size while maintaining the same level of accuracy of the original sequential genetic algorithm.

关键词： Instance-based parallel genetic algorithm instance reduction training set

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Fuzzy adaptive search method for parallel genetic algorithm with island combination process

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2006年第1期41卷 59-73页

作者： Maeda, Y Ishita, M Li, Q Univ Fukui Dept Human & Artificial Intelligent Syst Fac Engn Fukui 9108507 Japan Univ Fukui Dept Human & Artificial Intelligent Syst Grad Sch Engn Fukui 9108507 Japan

genetic algorithms (GAs) pose several problems. Probably, the most important one is that the search ability of ordinary GAs is not always optimal in the early and final stages of the search because of fixed GA parameters. To solve this problem, we proposed the fuzzy adaptive search method for genetic algorithms (FASGA) that is able to time the genetic parameters according to the search stage by the fuzzy reasoning. In this paper, a fuzzy adaptive search method for parallel genetic algorithms (FASPGA) is proposed. in which the high-speed search ability of fuzzy adaptive tuning by FASGA is combined with the high-quality solution finding capacity of parallel genetic algorithms. The proposed method offers improved search performance, and produces high-quality solutions. Moreover, we also propose FASPGA with an operation of combining dynamically sub-populations (C-FASPGA) which combines two elite islands in the final stage of the evolution to find a better Solution as early as possible. Simulations are performed to confirm the efficiency of the proposed method, which is shown to be superior to both ordinary and parallel genetic algorithms. (c) 2005 Elsevier Inc. All rights reserved.

关键词： parallel genetic algorithm fuzzy reasoning adaptive search migration rate

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Economic Power Dispatch with Discontinuous Fuel Cost Functions using Improved parallel PSO

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2010年第1期5卷 45-53页

作者： Mahdad, Belkacem Bouktir, T. Srairi, K. El Benbouzid, M. Biskra Univ Dept Elect Engn Biskra Algeria Univ Brest Lab Brestois Mecan & Syst Brest France

This paper presents an improved parallel particle swarm optimization approach (IPPSO) based decomposed network for economic power dispatch with discontinuous fuel cost functions. The range of partial power demand corresponding to the partial output powers near the global optimal solution is determined by a flexible decomposed network strategy and then the final optimal solution is obtained by parallel Particle Swarm Optimization. The proposed approach tested on 6 generating units with smooth cost function, and to 26-bus (6 generating units) with consideration of prohibited zone effect, the simulation results compared with recent global optimization methods (Bee-OPF, GA, NITS, SA, PSO). From the different case studies, it is observed that the proposed approach provides qualitative solution with less computational time compared to various methods available in the literature survey.

关键词： parallel genetic algorithm Decomposed Network PSO Economic dispatch Optimal power flow System security Planning and control

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Dynamic strategy based fast decomposed GA coordinated with FACTS devices to enhance the optimal power flow

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ENERGY CONVERSION AND MANAGEMENT 2010年第7期51卷 1370-1380页

作者： Mahdad, Belkacem Bouktir, T. Srairi, K. EL Benbouzid, M. Univ Biskra Dept Elect Engn Biskra 07000 Algeria Dept Elect Engn Oum El Bouaghi 04000 Algeria Univ Brest Lab Brestois Mecan & Syst Brest France

Under critical situation the main preoccupation of expert engineers is to assure power system security and to deliver power to the consumer within the desired index power quality. The total generation cost taken as a secondary strategy. This paper presents an efficient decomposed GA to enhance the solution of the optimal power flow (OPF) with non-smooth cost function and under severe loading conditions. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two sub problems are proposed to coordinate the OPF problem under different loading conditions: the first sub problem related to the active power planning under different loading factor to minimize the total fuel cost, and the second sub problem is a reactive power planning designed based in practical rules to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the proposed algorithm tested on IEEE 30-Bus, 26-Bus and IEEE 118-Bus under different loading conditions and compared with global optimization methods (GA, EGA, FGA, PSO, MTS, MDE and ACO) and with two robust simulation packages: PSAT and MAT-POWER. The results show that the proposed approach can converge to the near solution and obtain a competitive solution at critical situation and with a reasonable time. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Decomposed network parallel genetic algorithm System loadability FACTS SVC Optimal power flow System security Planning and control

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Integrating berth allocation and quay crane assignments

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2010年第6期46卷 975-990页

作者： Chang, Daofang Jiang, Zuhua Yan, Wei He, Junliang Shanghai Maritime Univ Minist Educ Container Supply Chain Technol Engn Res Ctr Shanghai 200135 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China

In this study, a dynamic allocation model using objective programming for berth allocation and quay crane assignments was preliminarily developed based on rolling-horizon approach. Afterwards, a hybrid parallel genetic algorithm (HPGA), which combined parallel genetic algorithm (PGA) and heuristic algorithm, was employed to resolve the proposed model. Furthermore, a simulation was conducted to evaluate the HPGA and to execute relevant gene repair techniques. Eventually, the numerical experiments on a specific container terminal were applied to illustrate the proposed models and algorithms. In so doing, the effectiveness of the proposed approach was verified. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.

关键词： Berth allocation Quay crane assignments Objective programming Rolling-horizon technique Heuristic algorithm parallel genetic algorithm

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The Implementation and Improvements of genetic algorithm for Job-Shop Scheduling Problems 10

The Implementation and Improvements of Genetic Algorithm for...

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12th Annual genetic and Evolutionary Computation Conference (GECCO)

作者： Parinov, Oleg Higher Sch Econ Moscow Russia

ISBN: (纸本)9781450300735

In this paper, we describe our implementation of genetic algorithm for job-shop scheduling problem as a processing core for production plan optimization system. We have implemented several well-known improvements like parallel structure and some operations based on Giffler and Thompson algorithm, and also created two our own hypotheses that were fully tested on several benchmarks and then one of them - the concept of "clever" crises has successfully implemented and showed considerable improvement of genetic algorithm itself.

关键词： genetic algorithm job-shop scheduling problem parallel genetic algorithm

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A Communication Scheme for an Experimental Grid in the Resolution of VRPTW using an Evolutionary algorithm

A Communication Scheme for an Experimental Grid in the Resol...

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7th IEEE Electronics, Robotics and Automotive Mechanics Conference (CERMA)

作者： Rodriguez-Leon, Abelardo Antonio Cruz-Chavez, Marco Rivera-Lopez, Rafael Avila-Melgar, Erika-Yesenia Juarez-Perez, Fredy Cruz-Rosales, Martin-Heriberto Inst Tecnol Veracruz Dept Sistemas & Computac Calzada Miguel Angel Quevedo 2779 Veracruz Veracruz Mexico Autonomous Univ Morelos State CIICAp Chamilpa Morelos Mexico Autonomous Univ Morelos State FCAeI Chamilpa Morelos Mexico

ISBN: (纸本)9780769542041

In this work is defined a scheme of two stages for sending population segments of one parallel genetic algorithm (PGA) to the nodes of an experimental grid called "Tarantula miniGrid". The technique used to link the clusters and to configure the Tarantula miniGrid is described and the effects of latency in the communications between these clusters and the behavior of the speedup are discussed. The PGA is used for solving instances of the Vehicle Routing Problem with Time Windows (VRPTW). Results of this approach show that this communication scheme increases the efficiency of PGA in a grid environment and produces greater exploration and exploitation of the solution space of VRPTW.

关键词： Experimental Grid parallel Computing parallel genetic algorithm Tarantula miniGrid Vehicle Routing Problem evolutionary algorithm genetic algorithms grid environment parallel genetic algorithm pattern clustering time windows transportation vehicle routing

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Interval-based Initialization Method for Permutation-based Problems

Interval-based Initialization Method for Permutation-based P...

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2010 IEEE World Congress on Computational Intelligence

作者： Mehdi, Malika Melab, Nouredine Talbi, El-Ghazali Bouvry, Pascal Univ Luxembourg Fac Comp Sci & Commun 6 Rue Richard Coudenhove L-1220 Kalergi Luxembourg INRIA Futurs Parc Sci Haute Borne 40 F-59650 Villeneuve Dascq France

ISBN: (纸本)9781424481262

When dealing with exponential search spaces and when no special knowledge is available on global optima, initial populations for population-based meta-heuristics should be uniformly distributed on the search space in order to sample basins of attraction of all local optima. In this paper, we propose a new initialization strategy for permutation problems. The new method is based on an original tree representation of the search space. Such representation was previously used for exact methods but never for meta-heuristics. The proposed method has been tested using a parallel genetic algorithm implemented in the ParadisEO framework and experimented on the Nationwide Grid5000 experimental grid using the Q3AP (3D QAP) permutation problem. The preliminary results are promising.

关键词： ParadisEO framework exponential search spaces genetic algorithms interval-based initialization method nationwide Grid5000 experimental grid parallel genetic algorithm permutation-based problems population-based metaheuristics search space representation statistical analysis tree searching

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Human-Oriented Image Retrieval of Optimized Multi-feature via genetic algorithm 1

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International Conference on Information Computing and Applications

作者： Liu, Mingsheng Li, Jianhua Liu, Hui Handan Coll Sch Informat Engn Handan Peoples R China Shijiazhuang TieDao Univ Sch Informat Sci & Technol Shijiazhuang Peoples R China

ISBN: (数字)9783642161674

ISBN: (纸本)9783642161667

There have been two problems in the implementation of a content-based image retrieval (CBIR) system in web. One is the absence of a standardized way to describe image content, the other is the disregard for the special needs of individual users To address these two problems, in this paper, a human-oriented CBIR system is presented which is implemented by applying MPEG-7 descriptors. In the new system, a multi-feature space is established and both homogeneous texture descriptor and color layout descriptor are used. Since there are difference in human perceptions of color and texture, in order to successfully retrieve an image which caters to the users, PGA (parallel genetic algorithm) is employed to adjust the weight of each feature space. The experimental evidence shows that the system is robust in general format by using MPEG-7 and it is capable of matching the user profile as well.

关键词： content-based image retrieval human preference MPEG-7 parallel genetic algorithm

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Evolutionary Quantum Logic Synthesis of Boolean Reversible Logic Circuits Embedded in Ternary Quantum Space using Structural Restrictions

Evolutionary Quantum Logic Synthesis of Boolean Reversible L...

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2010 IEEE World Congress on Computational Intelligence

作者： Lukac, Martin Perkowski, Marek Kameyama, Michitaka Tohoku Univ Dept Informat Sci Sendai Miyagi 980 Japan Portland State Univ Dept Elect Engn Portland OR USA

ISBN: (纸本)9781424481262

It has been experimentally proven that realizing universal quantum gates using higher-radices logic is practically and technologically possible. We developed a parallel genetic algorithm that synthesizes Boolean reversible circuits realized with a variety of quantum gates on qudits with various radices. We describe the experiments that we conducted using GPU programming. Various approaches to fitness function formulation were applied to obtain various realizations of well known universal Boolean reversible quantum gates.

关键词： Boolean reversible logic circuits GPU programming computer graphic equipment coprocessors evolutionary quantum logic synthesis fitness function formulation logic circuits parallel genetic algorithm radices logic structural restrictions ternary quantum space universal quantum gates

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