abstract

An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics

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Daniele MaccagnolaAuthors Info & Claims

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Pages 137 - 138

https://doi.org/10.1145/2464576.2464644

Published: 06 July 2013 Publication History

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Abstract

In the last few years researchers have dedicated several efforts to the definition of Genetic Programming (GP) [?] systems based on the semantics of the solutions, where by semantics we generally intend the behavior of a program once it is executed on a set of inputs, or more particularly the set of its output values on input training data (this definition has been used, among many others, for instance in [?, ?, ?, ?]). In particular, new genetic operators, called geometric semantic operators, have been proposed by Moraglio et al. [?]. They are defined s follows:

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Bakurov IBuzzelli MCastelli MVanneschi LSchettini R(2021)General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in PythonApplied Sciences10.3390/app1111477411:11(4774)Online publication date: 23-May-2021
https://doi.org/10.3390/app11114774
Cagnoni SCastelli M(2018)Special Issue on Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern RecognitionAlgorithms10.3390/a1103002511:3(25)Online publication date: 28-Feb-2018
https://doi.org/10.3390/a11030025
Miranda LOliveira LMartins JPappa GBosman P(2017)How noisy data affects geometric semantic genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071300(985-992)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071300
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Published In

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

July 2013

1798 pages

ISBN:9781450319645

DOI:10.1145/2464576

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 06 July 2013

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GECCO '13

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SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Bakurov IBuzzelli MCastelli MVanneschi LSchettini R(2021)General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in PythonApplied Sciences10.3390/app1111477411:11(4774)Online publication date: 23-May-2021
https://doi.org/10.3390/app11114774
Cagnoni SCastelli M(2018)Special Issue on Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern RecognitionAlgorithms10.3390/a1103002511:3(25)Online publication date: 28-Feb-2018
https://doi.org/10.3390/a11030025
Miranda LOliveira LMartins JPappa GBosman P(2017)How noisy data affects geometric semantic genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071300(985-992)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071300
Sanchez-Gutierrez GDominguez-Soberanes JRodriguez-Serrano GEscalona-Buendia HSanchez CGutierrez SGraff M(2017)Selecting crackling product based on sensory analysis by different statistical data approaches2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)10.1109/ROPEC.2017.8261639(1-6)Online publication date: Nov-2017
https://doi.org/10.1109/ROPEC.2017.8261639
Sanchez CGraff M(2017)Semantic genetic operators based on a selection mechanism tailored for the root function2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)10.1109/ROPEC.2017.8261638(1-6)Online publication date: Nov-2017
https://doi.org/10.1109/ROPEC.2017.8261638
Nguyen QPham TNguyen XMcdermott J(2016)Subtree semantic geometric crossover for genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-015-9253-517:1(25-53)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1007/s10710-015-9253-5

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New geometric semantic operators in genetic programming: perpendicular crossover and random segment mutation

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