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Optimal Reservoir Operation Using Multi-Objective Evolutionary Algorithm

M. Janga Reddy, D. Nagesh Kumar
<span title="2006-06-15">2006</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ypz7yxt47vdererbomkxgowygi" style="color: black;">Water resources management</a> </i> &nbsp;
This paper presents a Multi-objective Evolutionary Algorithm (MOEA) to derive a set of optimal operation policies for a multipurpose reservoir system.  ...  To overcome the drawbacks faced by the classical methods for Multi-objective Optimization Problems (MOOP), this study employs a population based search evolutionary algorithm namely Multi-objective Genetic  ...  Conclusion In this study, a Multi-objective Genetic Algorithm (MOGA) approach has been applied for optimization of a multi-objective reservoir operation problem.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11269-005-9011-1">doi:10.1007/s11269-005-9011-1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gzwv5kuatfaylljvbvpt3dvbne">fatcat:gzwv5kuatfaylljvbvpt3dvbne</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809035212/http://civil.iisc.ernet.in/~nagesh/pubs/29_WARM_Reddy_MOEA_Dec06.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b8/26/b826050af46653ad2e1174fe23f0d15dbe42b06d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11269-005-9011-1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

An Efficient Multi-objective Evolutionary Approach for Solving the Operation of Multi-Reservoir System Scheduling in Hydro-Power Plants [article]

C.G. Marcelino, G.M.C. Leite, C.A.D.M Delgado, L.B. de Oliveira, E.F. Wanner, S. Jiménez-Fernández, S. Salcedo-Sanz
<span title="2021-07-20">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained  ...  This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system - a cascade-based operation scenario.  ...  Conclusions and final remarks In this paper we have proposed a novel hybrid algorithm for multi-objective optimization, the Multi-objective Evolutionary Swarm Hybridization -MESH.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.09718v1">arXiv:2107.09718v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ay34ty3p7zhglagtpupg4ukunm">fatcat:ay34ty3p7zhglagtpupg4ukunm</a> </span>
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Enhanced Innovized Repair Operator for Evolutionary Multi- and Many-objective Optimization [article]

Sukrit Mittal and Dhish Kumar Saxena and Kalyanmoy Deb and Erik Goodman
<span title="2020-11-21">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
"Innovization" is a task of learning common relationships among some or all of the Pareto-optimal (PO) solutions in multi- and many-objective optimization problems.  ...  We then propose to use the resulting ML model as an additional innovized repair (IR2) operator to be applied on offspring solutions created by the usual genetic operators, as a novel mean of improving  ...  Since evolutionary algorithms use a population of solutions in each iteration, their extensions -evolutionary multi-objective optimization S.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.10760v1">arXiv:2011.10760v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cknsmmktnzcqlgkgbhpmqsuihm">fatcat:cknsmmktnzcqlgkgbhpmqsuihm</a> </span>
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Agent-Based Co-operative Co-evolutionary Algorithms for Multi-objective Portfolio Optimization [chapter]

Rafał Dreżewski, Krystian Obrocki, Leszek Siwik
<span title="">2010</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/oqt3yqhkxvcubnonwg57er4tdq" style="color: black;">Studies in Computational Intelligence</a> </i> &nbsp;
In this chapter agent-based versions of multi-objective co-operative co-evolutionary algorithms are presented and applied to portfolio optimization problem.  ...  individuals on the basis of tournaments (competitive co-evolutionary algorithms) or co-operation (co-operative co-evolutionary algorithms).  ...  One of the first attempts of applying agent-based co-operative co-evolutionary approach to multi-objective optimization problems was presented in [8] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-13950-5_5">doi:10.1007/978-3-642-13950-5_5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x3uoojthxfajtbixyx6o4istpy">fatcat:x3uoojthxfajtbixyx6o4istpy</a> </span>
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Single and Multi-objective Evolutionary Algorithms for the Coordination of Serial Manufacturing Operations

David Naso, Biagio Turchiano, Carlo Meloni
<span title="">2006</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/l5t2k3nljjfgffyvysjiwytjae" style="color: black;">Journal of Intelligent Manufacturing</a> </i> &nbsp;
Then, we focus on more complex case studies, for which no a priori indication on the optimal solutions is available, and perform an extensive comparison of the various approaches.  ...  Firstly, we validate the effectiveness of the proposed genetic algorithms considering a simple case study for which information about the optimal solution is available.  ...  and multi-objective optimization problems.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10845-005-6641-3">doi:10.1007/s10845-005-6641-3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lhadvcoulfbqvcuofd54amz5jy">fatcat:lhadvcoulfbqvcuofd54amz5jy</a> </span>
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Applications of multi-objective evolutionary algorithms to air operations mission planning

Brad Rosenberg, Marc Richards, John T. Langton, Sofya Tenenbaum, Daniel W. Stouch
<span title="">2008</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fdhfwmjdwjbvxo6zc7cdt5hi7q" style="color: black;">Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation - GECCO &#39;08</a> </i> &nbsp;
This paper presents several applications of multi-objective evolutionary algorithms for discovering suitable plans in the air operations domain, including dynamic targeting for air strike assets, intelligence  ...  Air operations mission planning is a complex task, growing ever more complex as the number, variety, and interactivity of air assets increases.  ...  Figure 1: Framework for Air Operations Planning Multi-objective Evolutionary Algorithms Evolutionary algorithms (EAs), and in particular, multi-objective evolutionary algorithms, have been successful  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1388969.1388994">doi:10.1145/1388969.1388994</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/gecco/RosenbergRLTS08.html">dblp:conf/gecco/RosenbergRLTS08</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/clavdji5r5enboa3ekbhefkmke">fatcat:clavdji5r5enboa3ekbhefkmke</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809112328/http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1879.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d4/0f/d40f96e3ce2a64a6ab83b9825f7f550b30647aea.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1388969.1388994"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Setup coordination between two stages of a production system: A multi-objective evolutionary approach

Carlo Meloni, David Naso, Biagio Turchiano
<span title="2006-08-23">2006</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kov3rwzipzf2dmxyohqwfpxrsm" style="color: black;">Annals of Operations Research</a> </i> &nbsp;
In particular, the paper compares three approaches based on different combinations of multi-objective evolutionary algorithms and local-search heuristics, using both small-size test instances and larger  ...  The analysis of the case-studies confirms the effectiveness of the evolutionary approaches, also enlightening the advantages and shortcomings of each considered algorithm.  ...  ., 2002) is a state-of-art multi-objective optimizer that can be applied to our problem.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10479-006-0065-0">doi:10.1007/s10479-006-0065-0</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/imgqztuwovgglb634xih7y5j4a">fatcat:imgqztuwovgglb634xih7y5j4a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809120506/http://dee.poliba.it/dee-web/nasoweb/papers/14.R.2006-AnnalsOR_MOGAscheduling_finale.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/59/b8/59b8ae3d0f562bd58ac27ae4d4566f2b68c2a119.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10479-006-0065-0"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Program

<span title="">2019</span> <i title="IEEE"> 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP) </i> &nbsp;
For optimal setting and coordinated operation of the DOCR relays, this paper proposes a modified objective function and is tested using a genetic algorithm to obtain the optimal settings for the IEEE 8  ...  In the present paper, a study about the optimal number of clusters is performed.  ...  For validation, the algorithm is simulated using MATLAB programming and the results are discussed. pp. 337-342 T3S6.4 A Case Study on Energy Efficient Green Building with New Intelligent Techniques Used  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/isap48318.2019.9065935">doi:10.1109/isap48318.2019.9065935</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n5yuhvudcfemzhr4tazxeei4fi">fatcat:n5yuhvudcfemzhr4tazxeei4fi</a> </span>
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Function optimization using cartesian genetic programming

Julian F. Miller, Maktuba Mohid
<span title="">2013</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fdhfwmjdwjbvxo6zc7cdt5hi7q" style="color: black;">Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion - GECCO &#39;13 Companion</a> </i> &nbsp;
In function optimization one tries to find a vector of real numbers that optimizes a complex multi-modal fitness function.  ...  Although evolutionary algorithms have been used extensively to solve such problems, genetic programming has not.  ...  RESULTS Vesterstrom compared the performance of Differential evolution (DE), Particle Swarm Optimization (PSO) and an evolutionary algorithm (SEA) on a series of benchmark functions [6] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2464576.2464646">doi:10.1145/2464576.2464646</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/gecco/MillerM13.html">dblp:conf/gecco/MillerM13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tddkndm2f5gqfa6jx426jhsc4m">fatcat:tddkndm2f5gqfa6jx426jhsc4m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160828042958/http://cartesiangp.co.uk/papers/gecco2013-miller.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/62/53/62537d68d3925441fa23ff58be705744611cbb93.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2464576.2464646"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Multi-objective design of quantum circuits using genetic programming [article]

Moein Sarvaghad-Moghaddam
<span title="2017-03-14">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Genetic programming is a subfield of evolutionary computing in which computer programs evolve to solve studied problems.  ...  In this paper for the first time, a multi-objective approach has been provided to design quantum circuits using genetic programming that considers the depth and the cost of nearest neighbor metrics in  ...  Genetic programming [8] is a subfield of evolutionary computing in which computer programs evolve to solve the studied problem.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1604.00642v3">arXiv:1604.00642v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e6vozvy43fag3hfmsztj7q6aua">fatcat:e6vozvy43fag3hfmsztj7q6aua</a> </span>
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Computational Complexity Analysis of Multi-Objective Genetic Programming [article]

Frank Neumann
<span title="2012-03-22">2012</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Furthermore, we study the expected time until population-based multi-objective genetic programming algorithms have computed the Pareto front when taking the complexity of a syntax tree as an equally important  ...  We consider generalizations of ORDER and MAJORITY and present a computational complexity analysis of (1+1) GP using multi-criteria fitness functions that take into account the original objective and the  ...  Acknowledgement The author thanks Christian Igel, Aneta Neumann, Una-May O'Reilly, and Markus Wagner for interesting discussions on the topic of this paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1203.4881v1">arXiv:1203.4881v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5nntks2stncu7fkqrx4ovqwag4">fatcat:5nntks2stncu7fkqrx4ovqwag4</a> </span>
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Dynamic synthesis of program invariants using genetic programming

Luigi Cardamone, Andrea Mocci, Carlo Ghezzi
<span title="">2011</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2dmkezwdzrcofn7zdzzte2h75i" style="color: black;">2011 IEEE Congress of Evolutionary Computation (CEC)</a> </i> &nbsp;
In this paper, we present a preliminary approach that integrates genetic programming into the synthesis of invariant formula that describes the behavior of a loop.  ...  Symbolic program manipulation plays a key role in program comprehension and verification.  ...  Genetic Programming Genetic programming (GP) is a branch of Evolutionary Computation where evolutionary algorithms are applied to optimize computer programs or mathematical formulas.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cec.2011.5949677">doi:10.1109/cec.2011.5949677</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cec/CardamoneMG11.html">dblp:conf/cec/CardamoneMG11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ootfxzhcfjacrpx2bqi4nm2lue">fatcat:ootfxzhcfjacrpx2bqi4nm2lue</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160913154554/http://www.luigicardamone.it/tesi-pubblicazioni/cardamone2011gp.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e1/83/e183fd53847722ada7fe778d6ac771b3033bda35.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cec.2011.5949677"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

A Preliminary Study of Knowledge Transfer in Multi-Classification Using Gene Expression Programming

Tingyang Wei, Jinghui Zhong
<span title="2020-01-17">2020</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wrk3kouosrhcxiprcbguskdipu" style="color: black;">Frontiers in Neuroscience</a> </i> &nbsp;
Gene Expression Programming (GEP), a variant of Genetic Programming (GP), is a well established technique for automatic generation of computer programs.  ...  This paper employs evolutionary multitasking optimization paradigm in an existing GEP-based multi-classification framework, so as to alleviate the output conflict of each separate binary GEP classifier  ...  Hence, to add to a preliminary insight, this paper tries to employ knowledge transfer operations on different evolutionary operators in GEP.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnins.2019.01396">doi:10.3389/fnins.2019.01396</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32009880">pmid:32009880</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6978847/">pmcid:PMC6978847</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/26yir7dgsffzfo4idbcqr5wa2e">fatcat:26yir7dgsffzfo4idbcqr5wa2e</a> </span>
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Genetic programming

Alan Piszcz, Terence Soule
<span title="">2005</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fdhfwmjdwjbvxo6zc7cdt5hi7q" style="color: black;">Proceedings of the 2005 workshops on Genetic and evolutionary computation - GECCO &#39;05</a> </i> &nbsp;
In this paper we study three structure altering mutation techniques using parametric analysis on a problem with scalable complexity.  ...  Genetic programming environments have few means for a priori determination of appropriate parameters values.  ...  Eiben et al. examines parameter settings for mutation, crossover, evaluation functions, replacement operator or survivor selection, and population size in evolutionary algorithms.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1102256.1102309">doi:10.1145/1102256.1102309</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/gecco/PiszczS05.html">dblp:conf/gecco/PiszczS05</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dsglpxwf5vdt7geqqujw54okdi">fatcat:dsglpxwf5vdt7geqqujw54okdi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170922030037/http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0220.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7c/7d/7c7d1c7c97b9cd22d5ad4a283895c56f3a56e92b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1102256.1102309"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Distributed scheduling with decomposed optimization criterion: Genetic programming approach [chapter]

Franciszek Seredyński, Jacek Koronacki, Cezary Z. Janikow
<span title="">1999</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Its main innovation lies in evolving a decomposition of the global optimization criteria. For this purpose a program graph is interpreted as a multi-agent system.  ...  To make the algoritm trully distributed, decomposition of the global optimization criterion into local criteria is proposed. This decomposition is evolved with genetic programming.  ...  We propose an approach t o m ultiprocessor scheduling based on a multi-agent interpretation of the parallel program.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/bfb0097900">doi:10.1007/bfb0097900</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ynin5li5ordv3bdxh5l6hhsavy">fatcat:ynin5li5ordv3bdxh5l6hhsavy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809073057/http://ipdps.cc.gatech.edu/1999/biosp3/seredyns.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b7/3f/b73ffd331da7cb1ae377b87d2d6059e593c0f83f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/bfb0097900"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>
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