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Genetic programming theory

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Riccardo PoliAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computation

Pages 2559 - 2588

https://doi.org/10.1145/1388969.1389068

Published: 12 July 2008 Publication History

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Sierra-Vélez JMacias DVial AGiraldo M(2023)Retrieving the Refractive Index of a Biological Material via Symbolic Regression19th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics10.1007/978-3-031-37132-5_48(377-384)Online publication date: 16-Jul-2023
https://doi.org/10.1007/978-3-031-37132-5_48
Aileni RSuciu GSuciu VPasca SStrungaru R(2018)Health Monitoring Using Wearable Technologies and Cognitive Radio for IoTCognitive Radio, Mobile Communications and Wireless Networks10.1007/978-3-319-91002-4_6(143-165)Online publication date: 31-Jul-2018
https://doi.org/10.1007/978-3-319-91002-4_6
Lino ARocha ÁSizo A(2016)A Proposal for Automatic Evaluation by Symbolic Regression in Virtual Learning EnvironmentsNew Advances in Information Systems and Technologies10.1007/978-3-319-31232-3_81(855-865)Online publication date: 2-Mar-2016
https://doi.org/10.1007/978-3-319-31232-3_81
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Index Terms

Genetic programming theory
1. Theory of computation
  1. Design and analysis of algorithms

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Genetic Programming (GP) homologous crossovers are a group of operators, including GP one-point crossover and GP uniform crossover, where the offspring are created preserving the position of the genetic material taken from the parents. In this paper we ...

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GECCO '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computation

July 2008

1182 pages

ISBN:9781605581316

DOI:10.1145/1388969

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Sierra-Vélez JMacias DVial AGiraldo M(2023)Retrieving the Refractive Index of a Biological Material via Symbolic Regression19th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics10.1007/978-3-031-37132-5_48(377-384)Online publication date: 16-Jul-2023
https://doi.org/10.1007/978-3-031-37132-5_48
Aileni RSuciu GSuciu VPasca SStrungaru R(2018)Health Monitoring Using Wearable Technologies and Cognitive Radio for IoTCognitive Radio, Mobile Communications and Wireless Networks10.1007/978-3-319-91002-4_6(143-165)Online publication date: 31-Jul-2018
https://doi.org/10.1007/978-3-319-91002-4_6
Lino ARocha ÁSizo A(2016)A Proposal for Automatic Evaluation by Symbolic Regression in Virtual Learning EnvironmentsNew Advances in Information Systems and Technologies10.1007/978-3-319-31232-3_81(855-865)Online publication date: 2-Mar-2016
https://doi.org/10.1007/978-3-319-31232-3_81
Sebag M(2011)Nonstandard Criteria in Evolutionary LearningEncyclopedia of Machine Learning10.1007/978-0-387-30164-8_599(722-731)Online publication date: 2011
https://doi.org/10.1007/978-0-387-30164-8_599

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Bounding bloat in genetic programming

Using genetic programming for the induction of novice procedural programming solution algorithms

Exact Schema Theory and Markov Chain Models for Genetic Programming and Variable-length Genetic Algorithms with Homologous Crossover

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Bounding bloat in genetic programming

Using genetic programming for the induction of novice procedural programming solution algorithms

Exact Schema Theory and Markov Chain Models for Genetic Programming and Variable-length Genetic Algorithms with Homologous Crossover

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