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World Summit on genetic and Evolutionary Computation (GEC 09)

作者： Agrawal, Shubham Bose, Sumit Kumar Sundarrajan, Srikanth Univ Texas Austin Dept Mech Engn Austin TX 78712 USA

ISBN: (纸本)9781605583266

The advent of virtualization technologies encourages organizations to undertake server consolidation exercises for improving the overall server utilization and for minimizing the capacity redundancy within data-centers. Identifying complimentary workload patterns is a key to the success of server consolidation exercises and for enabling multi-tenancy within data-centers. Existing works either do not consider incompatibility constraints or performs poorly on the disjointed conflict graphs. The algorithm proposed in the current work overcomes the limitations posed by the existing solutions. The current work models the server consolidation problem as a vector packing problem with conflicts (VPC) and tries to minimize the number of servers used for hosting applications within data-centers and maximizes the packing efficiency of the servers utilized. This paper solves the problem using techniques inspired from grouping genetic algorithm (GGA) - a variant of the traditional genetic algorithm (GA). The algorithm is tested over varying scenarios which show encouraging results.

关键词： Vector Packing Problem with Conflicts (VPC) Enterprise Grid Virtualization grouping genetic algorithm Incompatibility Constraints

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grouping genetic algorithm for the Capacitated p-median Problem

Grouping Genetic Algorithm for the Capacitated <i>p</i>-medi...

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International Conference on Information and Digital Technologies (IDT)

作者： Janosikova, Ludmila Vasilovsky, Patrik Univ Zilina Fac Management Sci & Informat Univ 1 Zilina 01026 Slovakia

ISBN: (纸本)9781509056897

The paper suggests an adaptation of a grouping genetic algorithm for solving the capacitated p-median problem. We propose a new encoding of the individual solutions that enables an efficient implementation of the crossover operation. A hybrid metaheuristic that combines the grouping genetic algorithm with the post-processing solver is proposed as well. Numerical experiments performed on benchmark instances proved the predominance of the grouping encoding over the standard encoding.

关键词： facility location capacitated p-median problem grouping genetic algorithm linear programming

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grouping genetic algorithm for Data Clustering

Grouping Genetic Algorithm for Data Clustering

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2nd Swarm, Evolutionary and Memetic Computing Conference (SEMCCO 2011)

作者： Peddi, Santhosh Singh, Alok Univ Hyderabad Dept Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

ISBN: (纸本)9783642271717

Clustering can be visualized as a grouping problem as it consists of identifying finite set of groups in a dataset. grouping genetic algorithms are specially designed to handle grouping problems. As the clustering criteria such as minimizing the with-in cluster distance is high-dimensional, non-linear and multi-modal, many standard algorithms available in the literature for clustering tend to converge to a locally optimal solution and/or have slow convergence. Even genetic guided clustering algorithms which are capable of identifying better quality solutions in general are also not totally immune to these shortcomings because of their ad hoc approach towards clustering invalidity and context insensitivity. To remove these shortcomings we have proposed a hybrid steady-state grouping genetic algorithm. Computational results show the effectiveness of our approach.

关键词： grouping genetic algorithm Data Clustering Heuristic

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grouping genetic algorithm with efficient data structures for the university course timetabling problem

Grouping genetic algorithm with efficient data structures fo...

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8th International Conference on the Practice and Theory of Automated Timetabling, PATAT 2010

作者： Santos, Felipe Arenales Delbem, Alexandre C. B. University of Sao Paulo Sao Carlos SP Brazil

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Modular product design with grouping genetic algorithm - a case study

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COMPUTERS & INDUSTRIAL ENGINEERING 2004年第3期46卷 443-460页

作者： Kreng, VB Lee, TP Natl Cheng Kung Univ Sch Ind Management Tainan 701 Taiwan

Modular products are products that fulfill various functions through the combination of distinct modules. These detachable modules are constructed both according to the maximum physical and functional relations among components and maximizing the similarity of specifically modular driving forces. Accordingly, a non-linear programming is proposed to identify separable modules and simultaneously optimize the number of modules. This paper presents a systematic approach to accomplish modular product design in four major phases. Phase 1 is by means of functional and physical interaction analysis to format a component-to-component correlation matrix. Phase 2 is the exploration of design requirements to evaluate the relative importance of each modular driver. In phase 3, non-linear programming is used to formulate the objective function. In the final phase, a heuristic grouping genetic algorithm is adopted to search for the optimal or near-optimal modular architecture. This process and its application are illustrated by a real case of an electrical consumer product provided by an Original Design Manufacturer. The results demonstrate that the designer could direct a new approach to establish product modules according to the relative importance of modular drivers and the interaction among components. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： modular product design non-linear programming grouping genetic algorithm

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Cooperative parallel grouping genetic algorithm for the one-dimensional bin packing problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 125卷 157-170页

作者： Kucukyilmaz, Tayfun Kiziloz, Hakan Ezgi TED Univ Ankara Turkey Univ Turkish Aeronaut Assoc Ankara Turkey

Evolutionary algorithms have been reported to be efficient metaheuristics for the optimization of several NP Hard combinatorial optimization problems. In addition to their ability to solve difficult and complex problems in reasonable execution times, parallelized versions of evolutionary algorithms are reported to explore and exploit the problem search space more effectively than their sequential counterparts. The Island Model, where the population of a given run is divided into semi isolated subpopulations, is a popular parallelization approach for evolutionary algorithms such as grouping genetic algorithms (GGA). Although the nature of GGAs is very suitable for coarse-grained parallel processing, designing an Island-parallel model for them is not a straightforward task. Selecting the communication topology, deciding migration and assimilation strategies, adjusting the migration rate and frequency, and using efficient diversification techniques are some of the important issues that needs to be covered in a successful Island-parallel Model. In this study, we propose a novel, scalable Island parallel GGA (IPGGA) for the well-known combinatorial optimization Problem 1D Bin-Packing (1DBPP). We provide a thorough experimental evaluation of the parallel model and report significant improvements on the Hard28 problem instances by outperforming the state-of-the-art genetic algorithms. Additionally, we analyze and evaluate the parallelization parameters of IPGGA with an emphasis on problem search-space diversity and report several interesting results.

关键词： 1D bin-packing grouping genetic algorithm Parallel evolutionary algorithms Search space diversity Migration topology Assimilation strategy

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Flexible job shop scheduling with parallel machines using genetic algorithm and grouping genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第11期39卷 10016-10021页

作者： Chen, James C. Wu, Cheng-Chun Chen, Chia-Wen Chen, Kou-Huang Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu Taiwan Data Syst Consulting Co Taipei Taiwan Natl Cheng Kung Univ Dept Ind & Informat Management Tainan 70101 Taiwan China Univ Sci & Technol Dept Ind Engn & Management Taipei Taiwan

Based on genetic algorithm (GA) and grouping genetic algorithm (GGA), this research develops a scheduling algorithm for job shop scheduling problem with parallel machines and reentrant process. This algorithm consists of two major modules: machine selection module (MSM) and operation scheduling module (OSM). MSM helps an operation to select one of the parallel machines to process it. OSM is then used to arrange the sequences of all operations assigned to each machine. A real weapon production factory is used as a case study to evaluate the performance of the proposed algorithm. Due to the high penalty of late delivery in military orders and high cost of equipment investment, total tardiness, total machine idle time and makespan are important performance measures used in this study. Based on the design of experiments, the parameters setting for GA and GGA are identified. Simulation results demonstrate that MSM and OSM respectively using GGA and GA outperform current methods used in practice. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Flexible job shop Scheduling Parallel machine genetic algorithm grouping genetic algorithm Makespan Tardiness

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A new grouping genetic algorithm approach to the multiple traveling salesperson problem

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SOFT COMPUTING 2009年第1期13卷 95-101页

作者： Singh, Alok Baghel, Anurag Singh Univ Hyderabad Dept Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India Dept Elect Commun Jaipur 302001 Rajasthan India

The multiple traveling salesperson problem (MTSP) is an extension of the well known traveling salesperson problem (TSP). Given m > 1 salespersons and n > m cities to visit, the MTSP seeks a partition of cities into m groups as well as an ordering among cities in each group so that each group of cities is visited by exactly one salesperson in their specified order in such a way that each city is visited exactly once and sum of total distance traveled by all the salespersons is minimized. Apart from the objective of minimizing the total distance traveled by all the salespersons, we have also considered an alternate objective of minimizing the maximum distance traveled by any one salesperson, which is related with balancing the workload among salespersons. In this paper, we have proposed a new grouping genetic algorithm based approach for the MTSP and compared our results with other approaches available in the literature. Our approach outperformed the other approaches on both the objectives.

关键词： constrained optimization heuristic grouping genetic algorithm multiple traveling salesperson problem

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An adjustable grouping genetic algorithm for the design of cellular manufacturing system integrating structural and operational parameters

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JOURNAL OF MANUFACTURING SYSTEMS 2017年第Part1期44卷 115-142页

作者： Jawahar, N. Subhaa, R. Thiagarajar Coll Engn Dept Mech Engn Madurai 625015 Tamil Nadu India

This paper presents non-linear and linear formulations for the design of a Cellular Manufacturing Systems (CMS) modeled integrating structural and operational decision parameters, and a genetic algorithm (GA) based on self-regulating adaptive operators. The proposed CMS model evolves the structural design decisions of number of cells, and parts-machines assignment to cells, along with operational decisions of scheduling under machine duplications and alternate routings/cross-flow environments. The distinctive features of the CMS model under consideration are: i) integration of cost elements addressing both structural and operational issues in the design of CMS;ii) capable of evolving better CMS design decisions in terms of operational cost when compared to the literature part-machine grouping decisions;iii) suitable for variety of manufacturing system designs by relaxing the model constraints. Besides, this paper proposes a new variant of grouping genetic algorithm namely Adjustable grouping genetic algorithm (AGGA) that has features to adjust the coding suitable for machine duplication environment of the proposed CMS model and regulate genetic parameters towards convergence. It is shown, through comparisons with Simulated Annealing (SA) algorithm, Simple genetic algorithm (SGA) and also optimal solutions obtained via mathematical model relaxed to fixed number of cells, that AGGA is capable of evolving optimal or near optimal solutions in a computationally efficient manner. (C) 2017 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Cellular manufacturing system genetic algorithm Cell formation grouping genetic algorithm Adaptive parameters

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A hybrid grouping genetic algorithm for the registration area planning problem

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COMPUTER COMMUNICATIONS 2007年第10期30卷 2180-2190页

作者： James, Tabitha Vroblefski, Mark Nottingham, Quinton Virginia Polytech Inst & State Univ RB Pamplin Coll Business Dept Business Informat Technol Blacksburg VA 24061 USA Univ Arizona Dept Management Informat Syst Eller Coll Management Tucson AZ 85721 USA

With the growing use of mobile communication devices, the management of such technologies is of increasing importance. The registration area planning (RAP) problem examines the grouping of cells comprising a personal communication services (PCs) network into contiguous blocks in an effort to reduce the cost of managing the location of the devices operating on the network, in terms of bandwidth. This study introduces a hybridized grouping genetic algorithm (HGGA) to obtain cell formations for the RAP problem. The hybridization is accomplished by adding a tabu search-based improvement operator to a traditional grouping genetic algorithm (GGA). Results indicate that significant performance gains can be realized by hybridizing the algorithm, especially for larger problem instances. The HGGA is shown to consistently outperform the traditional GGA on problems of size greater than 19 cells. (c) 2007 Elsevier B.V. All rights reserved.

关键词： grouping genetic algorithm Registration area planning PCS networks

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