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On the architecture and implementation of tree-based genetic programming in HeuristicLab

Authors:

Michael AffenzellerAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

Pages 101 - 108

https://doi.org/10.1145/2330784.2330801

Published: 07 July 2012 Publication History

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Abstract

This article describes the architecture and implementation of the genetic programming (GP) framework of HeuristicLab. In particular we focus on the core design goals, namely extensibility, usability, and performance optimization and explain our approach to reach these goals. The overall design, the encoding, interpretation, and evaluation of programs is described and code examples are given to explain core aspects of the framework. HeuristicLab is available as open source software at http://dev.heuristiclab.com.

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ElDarwich HMansouri IGarlock MWang S(2024)Predicting maximum deflection of N-Edged thin-shelled hyperbolic-Paraboloid umbrella using machine learning techniquesThin-Walled Structures10.1016/j.tws.2024.112412205(112412)Online publication date: Dec-2024
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Piringer DWagner SHaider CFohler ASilber SAffenzeller M(2023)Improving the Flexibility of Shape-Constrained Symbolic Regression with Extended ConstraintsComputer Aided Systems Theory – EUROCAST 202210.1007/978-3-031-25312-6_18(155-163)Online publication date: 10-Feb-2023
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Index Terms

On the architecture and implementation of tree-based genetic programming in HeuristicLab
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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Information

Published In

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

July 2012

1586 pages

ISBN:9781450311786

DOI:10.1145/2330784

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

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

Publication History

Published: 07 July 2012

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GECCO '12

Sponsor:

SIGEVO

GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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

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Oyeyi AKhan AHuyan JZhang WNi FTighe S(2024)Ensemble and evolutionary prediction of layers temperature in conventional and lightweight cellular concrete subbase pavementsInternational Journal of Pavement Engineering10.1080/10298436.2024.232252525:1Online publication date: 15-Mar-2024
https://doi.org/10.1080/10298436.2024.2322525
ElDarwich HMansouri IGarlock MWang S(2024)Predicting maximum deflection of N-Edged thin-shelled hyperbolic-Paraboloid umbrella using machine learning techniquesThin-Walled Structures10.1016/j.tws.2024.112412205(112412)Online publication date: Dec-2024
https://doi.org/10.1016/j.tws.2024.112412
Piringer DWagner SHaider CFohler ASilber SAffenzeller M(2023)Improving the Flexibility of Shape-Constrained Symbolic Regression with Extended ConstraintsComputer Aided Systems Theory – EUROCAST 202210.1007/978-3-031-25312-6_18(155-163)Online publication date: 10-Feb-2023
https://doi.org/10.1007/978-3-031-25312-6_18
Krauss OWagner M(2022)AmaruProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534016(1930-1937)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534016
Piringer DBloder BKronberger G(2022)Steel Phase Kinetics Modeling using Symbolic Regression2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)10.1109/SYNASC57785.2022.00059(327-330)Online publication date: Sep-2022
https://doi.org/10.1109/SYNASC57785.2022.00059
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Parra DGutiérrez AVelasco JGarnica OHidalgo J(2022)Combining the Properties of Random Forest with Grammatical Evolution to Construct Ensemble ModelsApplications of Evolutionary Computation10.1007/978-3-031-02462-7_5(61-76)Online publication date: 15-Apr-2022
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Gorsevski P(2021)An evolutionary approach for spatial prediction of landslide susceptibility using LiDAR and symbolic classification with genetic programmingNatural Hazards10.1007/s11069-021-04780-zOnline publication date: 27-May-2021
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Dorfmeister DKrauss OCoello Coello C(2020)Integrating heuristiclab with compilers and interpreters for non-functional code optimizationProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3398103(1580-1588)Online publication date: 8-Jul-2020
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