Tackling the Boolean Multiplexer Function Using a Highly Distributed Genetic Programming System

We demonstrate the effectiveness and power of the distributed GP platform, EC-Star, by comparing the computational power needed for solving an 11-multiplexer function, both on a single machine using a full-fitness evaluation method, as well as using distributed, age-layered, partial-fitness evaluati...

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Published inGenetic Programming Theory and Practice XII pp. 167 - 179
Main Authors Shahrzad, Hormoz, Hodjat, Babak
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 05.06.2015
SeriesGenetic and Evolutionary Computation
Subjects
Evolutionary computation
Fitness functions
Genetic algorithms
Genetic programming
Online AccessGet full text
ISBN331916029X
9783319160290
ISSN1932-0167
DOI10.1007/978-3-319-16030-6_10

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Abstract We demonstrate the effectiveness and power of the distributed GP platform, EC-Star, by comparing the computational power needed for solving an 11-multiplexer function, both on a single machine using a full-fitness evaluation method, as well as using distributed, age-layered, partial-fitness evaluations and a Pitts-style representation. We study the impact of age-layering and show how the system scales with distribution and tends towards smaller solutions. We also consider the effect of pool size and the choice of fitness function on convergence and total computation.
AbstractList We demonstrate the effectiveness and power of the distributed GP platform, EC-Star, by comparing the computational power needed for solving an 11-multiplexer function, both on a single machine using a full-fitness evaluation method, as well as using distributed, age-layered, partial-fitness evaluations and a Pitts-style representation. We study the impact of age-layering and show how the system scales with distribution and tends towards smaller solutions. We also consider the effect of pool size and the choice of fitness function on convergence and total computation.
Author Shahrzad, Hormoz
Hodjat, Babak
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Copyright Springer International Publishing Switzerland 2015
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DOI 10.1007/978-3-319-16030-6_10
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EISBN 3319160303
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Editor Riolo, Rick
Worzel, William P.
Kotanchek, Mark
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Snippet We demonstrate the effectiveness and power of the distributed GP platform, EC-Star, by comparing the computational power needed for solving an 11-multiplexer...
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StartPage 167
SubjectTerms Evolutionary computation
Fitness functions
Genetic algorithms
Genetic programming
Title Tackling the Boolean Multiplexer Function Using a Highly Distributed Genetic Programming System
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