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

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13 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion
PublisherACM Sigevo
Pages101-108
Number of pages8
ISBN (Print)9781450311786
DOIs
Publication statusPublished - 2012
Event14th International Conference on Genetic and Evolutionary Computation, GECCO'12 - Philadelphia, PA, United States
Duration: 7 Jul 201211 Jul 2012

Publication series

NameGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion

Conference

Conference14th International Conference on Genetic and Evolutionary Computation, GECCO'12
CountryUnited States
CityPhiladelphia, PA
Period07.07.201211.07.2012

Keywords

  • Genetic programming
  • HeuristicLab
  • Symbolic regression

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