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Scaling eCGA model building via data-intensive computing

Abhishek Verma, Xavier Llora, Shivaram Venkataraman, David E. Goldberg, Roy H. Campbell
2010 IEEE Congress on Evolutionary Computation  
Two different frameworks (Hadoop and MongoDB) are used to deploy MapReduce implementations of the compact and extended compact genetic algorithms.  ...  This paper shows how the extended compact genetic algorithm can be scaled using data-intensive computing techniques such as MapReduce.  ...  The views expressed are those of the authors only.  ... 
doi:10.1109/cec.2010.5586468 dblp:conf/cec/VermaLVGC10 fatcat:mzgehg2ownbw7hfp5hyqmjen2y

Knowledge management overview of feature selection problem in high-dimensional financial data: cooperative co-evolution and MapReduce perspectives

A N M Bazlur Rashid, Tonmoy Choudhury
2019 Problems and Perspectives in Management  
This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-the-art cooperative co-evolution and MapReduce-based feature selection techniques, and future  ...  Further, MapReduce, a programming model, offers a ready-to-use distributed, scalable, and fault-tolerant infrastructure for parallelizing the developed algorithm.  ...  This paper presents a knowledge management overview of evolutionary FS approaches and FS approaches based on CCEA and the MapReduce model with future research directions for FS problems. ).  ... 
doi:10.21511/ppm.17(4).2019.28 fatcat:76yr472o6rf7vm3torvgnxfcnm

Scaling Genetic Programming for data classification using MapReduce methodology

Nailah Al-Madi, Simone A. Ludwig
2013 2013 World Congress on Nature and Biologically Inspired Computing  
GP has been used to solve many classifications problems, however, its drawback is the long execution time.  ...  Our GP is implemented based on the MapReduce methodology (abbreviated as MRGP), in order to benefit from the MapReduce concept in terms of fault tolerance, load balancing, and data locality.  ...  ACKNOWLEDGMENT The authors acknowledge the support of the NDSU Advance FORWARD program sponsored by NSF HRD-0811239 and ND EPSCoR through NSF grant EPS-0814442.  ... 
doi:10.1109/nabic.2013.6617851 dblp:conf/nabic/Al-MadiL13 fatcat:yaiutgclsfeajivcwpf52rkkxi

Scaling Genetic Algorithms Using MapReduce

Abhishek Verma, Xavier Llorà, David E. Goldberg, Roy H. Campbell
2009 2009 Ninth International Conference on Intelligent Systems Design and Applications  
We describe the algorithm design and implementation of GAs on Hadoop, the open source implementation of MapReduce.  ...  Adding more resources would enable us to solve even larger problems without any changes in the algorithms and implementation.  ...  The inherent parallel nature of evolutionary algorithms makes them optimal candidates for parallelization (Cantú-Paz, 2000) .  ... 
doi:10.1109/isda.2009.181 dblp:conf/isda/VermaLGC09 fatcat:c3ik5hnnh5ghvfveeb5fz4flxm

Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce

Weiping Ding, Chin-Teng Lin, Witold Pedrycz
2018 IEEE Transactions on Cybernetics  
INTRODUCTION ensemble selection (MRFES) algorithm based on multilayer co-N RECENT years, massive amounts of data have become evolutionary consensus MapReduce (MCCM).  ...  of prediclarge-scale dataset problems with the complex noise and multiple tion with sufficient accuracy [2].  ...  ACKNOWLEDGMENT The authors would like to express their sincere appreciation to the anonymous reviewers for their insightful comments, which greatly improved the quality of this paper.  ... 
doi:10.1109/tcyb.2018.2859342 pmid:30130243 fatcat:lk7dgvlfhjcj3exex3ddlgss7q

A first attempt on global evolutionary undersampling for imbalanced big data

I. Triguero, M. Galar, H. Bustince, F. Herrera
2017 2017 IEEE Congress on Evolutionary Computation (CEC)  
A note on versions: The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version.  ...  As evolutionary algorithm, we focus on the widely-used CHC evolutionary algorithm [17] , which is modified to create a more compact representation of the chromosomes, and make use of distributed datasets  ...  The authors proposed a two- Fig. 1 : EUS local for extremely imbalanced datasets [16] level parallelization model where MapReduce was used to divide the problem into smaller subproblems over which EUS  ... 
doi:10.1109/cec.2017.7969553 dblp:conf/cec/TrigueroGBH17 fatcat:x3kavbn4indepf2o2ph3lkeege

An overview of recent distributed algorithms for learning fuzzy models in Big Data classification

Pietro Ducange, Michela Fazzolari, Francesco Marcelloni
2020 Journal of Big Data  
These algorithms have been generally implemented by using ad-hoc programming paradigms, such as MapReduce, on specific distributed computing frameworks, such as Apache Hadoop and Apache Spark.  ...  This is the so-called three-V's model of Big Data and it has been used for the first time by Douglas Laney in 2001 [3], to describe the data management in three-dimensions.  ...  and tools are available for approaching the problem of generating fuzzy classification models from Big Data.  ... 
doi:10.1186/s40537-020-00298-6 fatcat:vutg2g544rcbpfhthhleg5sffy

Evolutionary Neural Network Based Energy Consumption Forecast for Cloud Computing

Yong Wee Foo, Cindy Goh, Hong Chee Lim, Zhi-Hui Zhan, Yun Li
2015 2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)  
This paper reports an evolutionary computation based modeling and forecasting approach to this problem.  ...  The results, both in terms of forecasting speed and accuracy, suggest that the evolutionary neural network approach to energy consumption forecasting for cloud computing is highly promising.  ...  Evolutionary neural network (ENN) is a type of machine learning that uses the EC approach to train an artificial neural network to find the optimized structure to a problem.  ... 
doi:10.1109/icccri.2015.17 dblp:conf/icccri/FooGLZL15 fatcat:e34wwz7xrzehjeqgkdhriplynm

EFFICIENT DATA MINING TECHNIQUES FOR BIG DATA ANALYSIS: A SURVEY

M. Amsaveni, PG and Research Department of Computer Science Chikkanna Govt Arts College Tirupur,Tamil Nadu, India.
2018 International Journal of Advanced Research in Computer Science  
Technology revolution has been facilitating millions of people by generating tremendous data, resulting in big data.  ...  After that, a comparative and state-of-the-art analysis is carried out to identify the limitations in those methods.  ...  A feature selection algorithm called MapReduce for Evolutionary Feature Selection (MR-EFS) [10] was presented based on evolution computation that used MapReduce paradigm for big data classification.  ... 
doi:10.26483/ijarcs.v9i6.6348 fatcat:prkimvn4tnefjnw4ivthjiptq4

MapReduce as a general framework to support research in Mining Software Repositories (MSR)

Weiyi Shang, Zhen Ming Jiang, Bram Adams, Ahmed E. Hassan
2009 2009 6th IEEE International Working Conference on Mining Software Repositories  
As a proof-of-concept, we migrate J-REX, an optimized evolutionary code extractor, to run on Hadoop, an open source implementation of MapReduce.  ...  In this paper, we propose the use of MapReduce, a distributed computing platform, to support research in MSR.  ...  This MapReduce strategy permits an increasing number of Reducers that can work in parallel on the problem.  ... 
doi:10.1109/msr.2009.5069477 dblp:conf/msr/ShangJAH09 fatcat:ywcsgsasunbtpjznrzumasrrki

Big Graph Mining: Frameworks and Techniques

Sabeur Aridhi, Engelbert Mephu Nguifo
2016 Big Data Research  
This task consists on using data mining algorithms to discover interesting, unexpected and useful patterns in large amounts of graph data. It aims also to provide deeper understanding of graph data.  ...  It also gives a categorization of both distributed data mining and machine learning techniques, graph processing frameworks and large scale pattern mining approaches.  ...  The NIMBLE approach allows to compose parallel ML-DM algorithms using reusable (serial and parallel) building blocks that can be efficiently executed using MapReduce and other parallel programming models  ... 
doi:10.1016/j.bdr.2016.07.002 fatcat:sctq3qlbmndd3islrbugcxxzv4

How to apply de Bruijn graphs to genome assembly

Phillip E C Compeau, Pavel A Pevzner, Glenn Tesler
2011 Nature Biotechnology  
This type of graph can be used to solve the shortest superstring problem and is used in DNA sequencing.  ...  In this paper, Google described their distributed-memory approach, which follows the bulk synchronous parallel (BSP) model of computing rather than the parallel random access machine (PRAM) model traditionally  ... 
doi:10.1038/nbt.2023 pmid:22068540 pmcid:PMC5531759 fatcat:yvbmxbd2pnbg5ojtaqggieuiia

An Enhanced Memetic Algorithm for Feature Selection in Big Data Analytics with MapReduce

Umanesan Ramakrishnan, Nandhagopal Nachimuthu
2022 Intelligent Automation and Soft Computing  
Within the Spark system, the proposed EMO algorithm is applied and the experimental results claim that it is superior to other approaches.  ...  This paper introduces an improved memetic optimization (EMO) algorithm for FS in this perspective by using conditional criteria in large datasets.  ...  Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.  ... 
doi:10.32604/iasc.2022.017123 fatcat:6krdrupx7ndvtdthqgjii2t3z4

Heterogeneous Cloud Framework for Big Data Genome Sequencing

Chao Wang, Xi Li, Peng Chen, Aili Wang, Xuehai Zhou, Hong Yu
2015 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
The combination of hardware acceleration and MapReduce execution flow could greatly accelerate the task of aligning short length reads to a known reference genome.  ...  However, data sizes and ease of access for scientific researchers are growing and most current methodologies rely on one acceleration approach and so cannot meet the requirements imposed by explosive data  ...  To the best of our knowledge, this approach targeting NGS has not yet been fully studied. In this paper, we present a novel approach for solving NGS problems using a cloud based paradigm.  ... 
doi:10.1109/tcbb.2014.2351800 pmid:26357087 fatcat:52gw5e6o6jc5tefrr3xau3kdcm

Big Data Analytics in Bioinformatics: A Machine Learning Perspective [article]

Hirak Kashyap, Hasin Afzal Ahmed, Nazrul Hoque, Swarup Roy, Dhruba Kumar Bhattacharyya
2015 arXiv   pre-print
The machine learning methods used in bioinformatics are iterative and parallel. These methods can be scaled to handle big data using the distributed and parallel computing technologies.  ...  This paper addresses the issues and challenges posed by several big data problems in bioinformatics, and gives an overview of the state of the art and the future research opportunities.  ...  ACKNOWLEDGMENTS The authors would like to thank the Ministry of HRD, Govt. of India for funding as a Centre of Excellence with thrust area in Machine Learning Research and Big Data Analytics for the period  ... 
arXiv:1506.05101v1 fatcat:oix7d5hecbfgthzhepznwyi6fm
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