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
Schlagworte:
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
Evolutionary computing
FastFlow
Parallel design patterns
Algorithmic skeletons
Multi/many core architectures
ISSN:0885-7458, 1573-7640
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-015-0358-5