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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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parallel metaheuristics for workforce planning

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Journal of Mathematical Modelling and algorithms 2007年第3期6卷 509-528页

作者： Alba, Enrique Luque, Gabriel Luna, Francisco Department of Languages and Computational Sciences University of Málaga Málaga 29071 Spain

Workforce planning is an important activity that enables organizations to determine the workforce needed for continued success. A workforce planning problem is a very complex task requiring modern techniques to be solved adequately. In this work, we describe the development of three parallel metaheuristic methods, a parallel genetic algorithm, a parallel scatter search, and a parallel hybrid genetic algorithm, which can find high-quality solutions to 20 different problem instances. Our experiments show that parallel versions do not only allow to reduce the execution time but they also improve the solution quality. © 2007 Springer Science + Business Media B.V.

关键词： parallel genetic algorithm parallel hybrid genetic algorithm parallel metaheuristics parallel scatter search Workforce planning

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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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APPLYING genetic algorithmS TO FREQUENCY ASSIGNMENT PROBLEMS 3

APPLYING GENETIC ALGORITHMS TO FREQUENCY ASSIGNMENT PROBLEMS

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Conference on Neural and Stochastic Methods in Image and Signal Processing III

作者： CROMPTON, W HURLEY, S STEPHENS, NM UNIV WALES COLL CARDIFF DEPT COMP MATHCARDIFF CF24YNWALES

ISBN: (纸本)0819416282

This paper details the application of a parallel genetic algorithm to the air-ground-air frequency assignment problem. Preliminary results indicate that the technique is successful in finding acceptable assignments, satisfying over 90% of constraints, for realistically sized air- ground-air frequency assignment scenarios. Comparisons are made with a classical backtracking and forward checking heuristic algorithm which is shown to be inferior to the genetic algorithm in terms of the execution time required to find reasonable frequency assignments.

关键词： parallel genetic algorithm FREQUENCY ASSIGNMENT INTERFERENCE CONSTRAINTS NP-COMPLETE

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parallel Differential Evolution in the PGAS Programming Model Implemented with PCJ Java Library 11th

Parallel Differential Evolution in the PGAS Programming Mode...

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11th International Conference on parallel Processing and Applied Mathematics (PPAM)

作者： Gorski, Lukasz Rakowski, Franciszek Bala, Piotr Nicolaus Copernicus Univ Fac Math & Comp Sci Torun Poland Univ Warsaw Interdisciplinary Ctr Math & Computat Modelling Warsaw Poland

ISBN: (纸本)9783319321493;9783319321486

New ways to exploit parallelism of large scientific codes are still researched on. In this paper we present parallelization of the differential evolution algorithm. The simulations are implemented in Java programming language using PGAS programing paradigm enabled by the PCJ library. The developed solution has been used to test differential evolution on a number of mathematical function as well as to fine-tune the parameters of nematode's C. Elegans connectome model. The results have shown that a good scalability and performance was achieved with relatively simple and easy to develop code.

关键词： parallel processing Differential evolution parallel genetic algorithm PGAS Java

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High-stiffness, lock -and -key heat -reversible locator-snap systems for the design for disassembly

High-stiffness, lock -and -key heat -reversible locator-snap...

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作者： Shalaby, Mohammed Mounir University of Michigan

学位级别：Ph.D.

The use of joints that can disengage with minimum labor, part damage, and material contamination is critical to ensure effective service, part reuse, and material recycling. This dissertation develops a general computational method for designing lock-and-key heat-reversible locator-snap systems that satisfy the aforementioned requirements. The lock-and-key concept is like a security code that allows easy disassembly when the right procedure is followed. It is realized by double-latching snaps that require force within a certain range to disengage, and multiple snaps that require heating multiple locations at different temperatures to disengage. During disassembly, thermal expansion constrained by locators and temperature gradient along the wall thickness are exploited to realize the deformation required to release the snaps. A generic optimization problem is posed to find the orientations, numbers, and locations of locators and snaps, and the numbers, locations, and sizes of heating areas, which realize the release of snaps with minimum heating and maximum stiffness, while satisfying motion and structural requirements. Screw Theory is utilized to pre-calculate the set of feasible orientations of locators and snaps that are examined during optimization. Multi-Objective genetic algorithm (MOGA) is used for solving the posed generic optimization problem. A parallel version, using manager-worker scheme, with active load balancing is developed to solve the generic optimization problem efficiently. The proposed algorithm selects between two parallelization schemes based on the average objective function evaluation time and either divides the population evenly over all processors or sends small patches of the population to the idle workers. The proposed heat-reversible locator-snap systems are applied to different case studies ranging from automotive bodies to consumer electronics. The first case study deals with joining internal frames and external panels in automotive bo

关键词： Heat-Reversible Locator-Snap System Design for Disassembly parallel genetic algorithm Thesis

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A Reliable Weighted Feature Selection for Auto Medical Diagnosis 15

A Reliable Weighted Feature Selection for Auto Medical Diagn...

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15th IEEE International Conference on Industrial Informatics (INDIN)

作者： Sahebi, Golnaz Majd, Amin Ebrahimi, Masoumeh Plosila, Juha Tenhunen, Hannu Univ Turku Dept Future Technol Turku Finland Abo Akad Univ Dept Informat Technol Turku Finland KTH Royal Inst Technol Stockholm Sweden Univ Turku Turku Finland

ISBN: (纸本)9781538608371

Feature selection is a key step in data analysis. However, most of the existing feature selection techniques are serial and inefficient to be applied to massive data sets. We propose a feature selection method based on a multi-population weighted intelligent genetic algorithm to enhance the reliability of diagnoses in e-Health applications. The proposed approach, called PIGAS, utilizes a weighted intelligent genetic algorithm to select a proper subset of features that leads to a high classification accuracy. In addition, PIGAS takes advantage of multi-population implementation to further enhance accuracy. To evaluate the subsets of the selected features, the KNN classifier is utilized and assessed on UCI Arrhythmia dataset. To guarantee valid results, leave-one-out validation technique is employed The experimental results show that the proposed approach outperforms other methods in terms of accuracy and efficiency. The results of the 16-class classification problem indicate an increase in the overall accuracy when using the optimal feature subset. Accuracy achieved being 99.70% indicating the potential of the algorithm to be utilized in a practical auto-diagnosis system. This accuracy was obtained using only half of features, as against an accuracy of 66.76% using all the features.

关键词： Data Analysis Feature Selection K-Nearest Neighbor Classification Optimization parallel genetic algorithm E-Health

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A new SLA-aware Load Balancing Method in the Cloud using an Improved parallel Task Scheduling algorithm 6

A new SLA-aware Load Balancing Method in the Cloud using an ...

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6th IEEE International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Ashouraei, Mehran Khezr, Seyed Nima Benlamri, Rachid Navimipour, Nima Jafari Islamic Azad Univ Tabriz Branch Young Researchers & Elite Club Tabriz Iran Lakehead Univ Dept Software Engn Thunder Bay ON Canada Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran

ISBN: (纸本)9781538675038

Cloud computing as a novel and entirely internet-based computing platform is emerging and its tenacious challenges become more vivid. A parallel genetic algorithm based method for scheduling tasks with priorities is provided in this paper. The goal is to efficiently utilize resources and reduce resource wastage in cloud environments. This is achieved by improving the load balancing rate while better resources are selected to fulfill arrival tasks in a shorter time with lower task failure rate. To evaluate the proposed method, it is simulated using Matlab and compared with two existing methods, a hybrid Ant colony-honey method and a Round Robin (RR) based load balancing method. The results show that the proposed method has 9% - 31% lower energy usage, 14% - 37% lower migration rate and 13%- 17% better Service Level Agreement (SLA) in comparison with the Hybrid and RR method.

关键词： task scheduling load balancing parallel genetic algorithm restriction table

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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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