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IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

作者： Mishra, Shreya Vipsita, Swati IIIT Bhubaneswar Dept Comp Sc Engn Bhubaneswar 751003 Odisha India

ISBN: (纸本)9781467389884

Biclustering deals with creating a sub-matrix that shows a high similarity across both genes and conditions. Bi-clustering targets at identifying several biclusters that reveal potential local patterns from a microarray matrix. In this paper, initially sequential evolutionary algorithm (SEBI) is implemented and few drawbacks of the approach were identified. To overcome the drawbacks, parallel strategies such as condition based evolutionary biclustering (CBEB) and coarse grained parallel genetic algorithm (CgPGA) were implemented. To further improve the performance, a new parallel genetic algorithm using dynamic demes strategy is implemented. This method uses global parallelization (master-slave model) with coarse-grained GA with overlapping subpopulation model. The primary objective is to find biclusters with minimum overlapping, large row variance, low mean square residue (MSR) and covering almost every element of expression matrix, thus minimizing the overall fitness value. Sequential EA and condition based EA (CBEB) is implemented but it was observed that both took too much time to meet the stopping criteria. So, to improve the efficiency of the genetic algorithm (GA), parallel GA has been implemented with dynamic deme strategy to reduce the execution time of GA and find good quality biclusters. DdPGA yielded good quality biclusters and search space could be increased by implementing this strategy. This experiment was implemented on yeast Saccharamyces dataset.

关键词： Biclustering genetic algorithm parallel genetic algorithm Mean squared residue Volume Row variance

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Automatic Arabic Text Summarization for Large Scale Multiple Documents Using genetic algorithm and MapReduce 2

Automatic Arabic Text Summarization for Large Scale Multiple...

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2nd Palestinian International Conference on Information and Communication Technology (PICICT)

作者： Al Breem, Sulaiman N. Baraka, Rebhi S. Islamic Univ Gaza Informat Technol Dept Gaza Palestine Islamic Univ Gaza Fac Informat Technol Gaza Palestine

ISBN: (纸本)9781509065387

Multi document summarization focuses on extracting the most significant information from a collection of textual documents. Most summarization techniques require the data to be centralized, which may not be feasible in many cases due to computational and storage limitations. The huge increase of data emerging by the progress of technology and the various sources makes automatic text summarization of large scale of data a challenging task. We propose an approach for automatic text summarization of large scale Arabic multiple documents using genetic algorithm and MapReduce parallel programming model. The approach insures scalability, speed and accuracy in summary generation. It eliminates sentence redundancy and increases readability and cohesion factors between the sentences of summaries. The experiments resulted in acceptable precision and recall scores. This indicates that the system successfully identifies the most important sentences. In Addition to all to that, the approach provided up to 10 x speedup score, which is faster than on a single machine. Therefore, it can deal with large-scale datasets successfully. Finally, the efficiency score of the proposed approach indicates that the large data set utilizes the available resources up to 62%.

关键词： Text Summarization parallel genetic algorithm MapReduce Hadoop Text Mining

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Solving job shop scheduling problem using a hybrid parallel micro genetic algorithm

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APPLIED SOFT COMPUTING 2011年第8期11卷 5782-5792页

作者： Yusof, Rubiyah Khalid, Marzuki Hui, Gan Teck Yusof, Syafawati Md Othman, Mohd Fauzi Univ Teknol Malaysia Int Campus Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur 54100 Malaysia

The effort of searching an optimal solution for scheduling problems is important for real-world industrial applications especially for mission-time critical systems. In this paper, a new hybrid parallel GA (PGA) based on a combination of asynchronous colony GA (ACGA) and autonomous immigration GA (AIGA) is employed to solve benchmark job shop scheduling problem. An autonomous function of sharing the best solution across the system is enabled through the implementation of a migration operator and a "global mailbox". The solution is able to minimize the makespan of the scheduling problem, as well as reduce the computation time. To further improve the computation time, micro GA which works on small population is used in this approach. The result shows that the algorithm is able to decrease the makespan considerably as compared to the conventional GA. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Job shop scheduling problem parallel genetic algorithm Micro GA Asynchronous colony GA Autonomous immigration GA

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Designing Energy-Efficient Approximate Adders using parallel genetic algorithms

Designing Energy-Efficient Approximate Adders using Parallel...

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IEEE Southeast Conference (IEEE SoutheastCon)

作者： Naseer, Adnan Aquib Ashraf, Rizwan A. Dechev, Damian DeMara, Ronald F. Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32826 USA

ISBN: (纸本)9781467373005

Approximate computing involves selectively reducing the number of transistors in a circuit to improve energy savings. Energy savings may be achieved at the cost of reduced accuracy for signal processing applications whereby constituent adder and multiplier circuits need not generate a precise output. Since the performance versus energy savings landscape is complex, we investigate the acceleration of the design of approximate adders using parallelized genetic algorithms (GAs). The fitness evaluation of each approximate adder is explored by the GA in a non-sequential fashion to automatically generate novel approximate designs within specified performance thresholds. This paper advances methods of parallelizing GAs and implements a new parallel GA approach for approximate multi-bit adder design. A speedup of approximately 1.6-fold is achieved using a quad-core Intel processor and results indicate that the proposed GA is able to find adders which consume 63.8% less energy than accurate adders.

关键词： parallelism genetic algorithms parallel genetic algorithm inexact arithmetic units approximate computing adder variable accuracy low power adders error distance power consumption process variation delay power reduction

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Network Coding Oriented Topology Design Based on parallel genetic algorithm

Network Coding Oriented Topology Design Based on Parallel Ge...

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The Fourth International Joint Conference on Computational Science and Optimization(第四届计算科学与优化国际大会 CSO 2011)

作者： Jinkai Li Yun Pan Department of Computer Communication University of China Beijing China

The network coding technique is promising for saving bandwidth in multicast-based applications, and how to design multicast network topologies that are suite for efficiently supporting network coding becomes an important issue at present. In this paper, we at first formulate this problem as a special case of kconnected problem and then deal it with a parallel genetic algorithm.

关键词： multicast network coding parallel genetic algorithm topology design

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An Approach for Cloud Resource Scheduling Based on parallel genetic algorithm

An Approach for Cloud Resource Scheduling Based on Parallel ...

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2011 3rd International Conference on Computer Research and Development(ICCRD 2011)

作者： Zhongni Zheng,Rui Wang,Hai Zhong,Xuejie Zhang Department of Computer Science and Engineering, Yunnan University,Kunming,P.R.China,650091

Resource scheduling is a key process for clouds such as Infrastructure as a Service *** make the most efficient use of the resources,we propose an optimized scheduling algorithm to achieve the optimization or sub-optimization for cloud scheduling *** investigate the possibility to place the Virtual Machines in a flexible way to improve the speed of finding the best allocation on the premise of permitting the maximum utilization of resources. Mathematically,we consider the scheduling problem come down to an Unbalance Assignment *** scheduling policy achieved by parallel genetic algorithm which is much faster than traditional genetic *** experiments show that our method improved both the speed of resources allocation and the utilization of system resource.

关键词： resources scheduling parallel genetic algorithm cloud computing assignment problems

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Acceleration of the Multilayer Network Operator Method Using MPI for Mobile Robot Team Control Synthesis

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Procedia Computer Science 2017年 103卷 88-93页

作者： A.I. Diveev E.Yu. Shmalko D.N. Zakharov Federal Research Center “Computer Science and Control” of Russian Academy of Sciences 44 Vavilova street Moscow 119333 Russia Peoples’ Friendship University of Russia 6 Miklukho-Maklaya str. Moscow 117198 Russia

The paper is devoted to the problem of machine-made synthesis of control for robotic teams. The goal of synthesis is to find a multidimensional control function that depends on the current states of all robots. The synthesised control function provides any time the optimal control values to allow each robot achieving the objectives with the best value of functional quality. The approach is based on multilayer network operator method that belongs to a symbolic regression class. Formations of multi-robot systems require individual robots to satisfy their kinematic equations while constantly maintaining inter-robot dynamic constraints. Verification of these dynamic constraints on each iteration of the evolutionary algorithm greatly increases the computational costs of the numerical synthesis. In the paper we propose to accelerate existing designs through taking advantage of newest programming tools of MPI framework for automatic parallelization. Experiments show that our approach reduces greatly computational time.

关键词： group of robots symbolic regression network operator parallel genetic algorithm MPI

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Reconfiguration of Smart Distribution Systems With Time Varying Loads Using parallel Computing

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IEEE TRANSACTIONS ON SMART GRID 2016年第6期7卷 2713-2723页

作者： Asrari, Arash Lotfifard, Saeed Ansari, Meisam Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA Washington State Univ Energy Syst Innovat Ctr Pullman WA 99164 USA Pishgaman Niroo Isfahan Co Esfahan *** Iran

The problem of finding optimal configuration of automated/smart power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when the time varying nature of loads is taken into account. In this paper, a systematic approach is proposed to determine an optimal long-term reconfiguration schedule. To solve the optimization problem, a novel adaptive fuzzy-based parallel genetic algorithm (GA) is proposed that employs the concept of parallel computing in identifying the optimal configuration of the network. The integration of fuzzy logic into the proposed method enhances the efficiency of the parallel GA by adaptively modifying the migration rates among different processors during the optimization process. A computationally efficient graph encoding method based on Dandelion coding strategy is developed, which automatically generates radial topologies and prevents the construction of infeasible radial networks in the optimization process. In order to consider the dynamic behavior of the load and reduce the load condition scenarios over the year under study, fuzzy C-mean clustering method is utilized. Finally, the performance of the proposed method is demonstrated on a 119-bus distribution network, and is compared with that of conventional single GA and conventional parallel GA.

关键词： Dandelion encoding distribution network econfiguration parallel genetic algorithm radial structure

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parallel genetic algorithm based adaptive thresholding for image segmentation under uneven lighting conditions

Parallel Genetic Algorithm based adaptive thresholding for i...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kanungo, P. Nanda, P. K. Ghosh, A. CV Raman Coll Engn Dept E&TC IACV Lab Bhubaneswar 752054 Orissa India Siksha O Anusandhan Univ ITER Dept Elect Commun Bhubaneswar 751030 Orissa India Indian Stat Inst Ctr Soft Comp Res Kolkata 700108 India

ISBN: (纸本)9781424465880

In this paper, two adaptive thresholding schemes have been proposed. These two schemes are based on adaptive selection of windows based on the proposed window merging and window growing. Windows are selected based on the entropy and feature entropy criterion. PGA and MMSE based segmentation schemes have been proposed to segment the windows selected a priori. The efficacy of the proposed approaches have been compared with the Huang's pyramidal window merging approach. It is found that the proposed approaches exhibited improved performance in the context of accuracy of segmentation.

关键词： Image Segmentation parallel genetic algorithm Clustering Adaptive Thresholding Entropy

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An effective new island model genetic algorithm for job shop scheduling problem

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COMPUTERS & OPERATIONS RESEARCH 2016年第Mar.期67卷 132-142页

作者： Kurdi, Mohamed Adnan Menderes Univ Dept Comp Engn TR-09010 Aydin Turkey

This paper presents an effective new island model genetic algorithm to solve the well-known job shop scheduling problem with the objective of minimizing the makespan. To improve the effectiveness of the classical island model genetic algorithm, we have proposed a new naturally inspired evolution model and a new naturally inspired migration selection mechanism that are capable of improving the search diversification and delaying the premature convergence. In the proposed evolution model, islands employ different evolution methods during their self-adaptation phases, rather than employing the same methods. In the proposed migration selection mechanism, worst individuals who are least adapted to their environments migrate first, hoping in finding a better chance to live in a more suitable environment that imposes a more suitable self-adaptation method on them. The proposed algorithm is tested on 52 benchmark instances, with the proposed evolution model and migration selection mechanism, and without them using the classical alternatives, and also compared with other algorithms recently reported in the literature. Computational results verify the improvements achieved by the proposed evolution model and migration selection mechanism, and show the superiority of the proposed algorithm over the others in terms of effectiveness. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Job shop scheduling Island model parallel genetic algorithm Evolutionary computation

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