Pool Evolution: A Parallel Pattern for Evolutionary and Symbolic Computing

We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin. The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness func...

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Veröffentlicht in:International journal of parallel programming Jg. 44; H. 3; S. 531 - 551
Hauptverfasser: Aldinucci, Marco, Campa, Sonia, Danelutto, Marco, Kilpatrick, Peter, Torquati, Massimo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.06.2016
Springer Nature B.V
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ISSN:0885-7458, 1573-7640
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Abstract We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin. The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness function is optimized. The pattern has been demonstrated to be suitable for capturing and modeling the parallel patterns underpinning various evolutionary algorithms, as well as other parallel patterns typical of symbolic computation. In this paper we introduce the pattern, we discuss its implementation on modern multi/many core architectures and finally present experimental results obtained with FastFlow and Erlang implementations to assess its feasibility and scalability.
AbstractList We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin. The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness function is optimized. The pattern has been demonstrated to be suitable for capturing and modeling the parallel patterns underpinning various evolutionary algorithms, as well as other parallel patterns typical of symbolic computation. In this paper we introduce the pattern, we discuss its implementation on modern multi/many core architectures and finally present experimental results obtained with FastFlow and Erlang implementations to assess its feasibility and scalability.
We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin. The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness function is optimized. The pattern has been demonstrated to be suitable for capturing and modeling the parallel patterns underpinning various evolutionary algorithms, as well as other parallel patterns typical of symbolic computation. In this paper we introduce the pattern, we discuss its implementation on modern multi/many core architectures and finally present experimental results obtained with FastFlow and Erlang implementations to assess its feasibility and scalability.
Issue Title: Special Issue on High-Level Parallel Programming and Applications We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin. The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness function is optimized. The pattern has been demonstrated to be suitable for capturing and modeling the parallel patterns underpinning various evolutionary algorithms, as well as other parallel patterns typical of symbolic computation. In this paper we introduce the pattern, we discuss its implementation on modern multi/many core architectures and finally present experimental results obtained with FastFlow and Erlang implementations to assess its feasibility and scalability.
Author Danelutto, Marco
Kilpatrick, Peter
Aldinucci, Marco
Torquati, Massimo
Campa, Sonia
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  organization: Department of Computer Science, University of Torino
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  surname: Campa
  fullname: Campa, Sonia
  organization: Department of Computer Science, University of Pisa
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  givenname: Marco
  surname: Danelutto
  fullname: Danelutto, Marco
  email: marco.danelutto@unipi.it
  organization: Department of Computer Science, University of Pisa
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  givenname: Peter
  surname: Kilpatrick
  fullname: Kilpatrick, Peter
  organization: Department of Computer Science, Queen’s University Belfast
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  givenname: Massimo
  surname: Torquati
  fullname: Torquati, Massimo
  organization: Department of Computer Science, University of Pisa
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Cites_doi 10.1109/TEVC.2011.2132725
10.1504/IJICA.2007.013403
10.1016/j.parco.2003.12.002
10.1145/1562764.1562783
10.1109/DEXA.2013.32
10.1002/(SICI)1099-0526(199903/04)4:4<31::AID-CPLX5>3.3.CO;2-W
10.1007/978-3-642-23397-5_17
10.1109/PDP.2014.13
10.23919/ACC.1993.4793307
10.1007/978-3-540-74466-5_49
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Multi/many core architectures
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SubjectTerms Algorithms
Analysis
Architecture (computers)
Central processing units
Computation
Computer programming
Computer Science
CPUs
Evolution
Evolutionary algorithms
Feasibility
Genetic algorithms
Mathematical analysis
Mathematical models
Optimization
Parallel processing
Pools
Population
Processor Architectures
Programmers
Semantics
Software Engineering/Programming and Operating Systems
Studies
Theory of Computation
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Title Pool Evolution: A Parallel Pattern for Evolutionary and Symbolic Computing
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Volume 44
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