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Schema Analysis in Tree-Based Genetic Programming

  • Bogdan BurlacuEmail author
  • Michael Affenzeller
  • Michael Kommenda
  • Gabriel Kronberger
  • Stephan Winkler
Conference paper
Part of the Genetic and Evolutionary Computation book series (GEVO)

Abstract

In this chapter we adopt the concept of schemata from schema theory and use it to analyze population dynamics in genetic programming for symbolic regression. We define schemata as tree-based wildcard patterns and we empirically measure their frequencies in the population at each generation. Our methodology consists of two steps: in the first step we generate schemata based on genealogical information about crossover parents and their offspring, according to several possible schema definitions inspired from existing literature. In the second step, we calculate the matching individuals for each schema using a tree pattern matching algorithm. We test our approach on different problem instances and algorithmic flavors and we investigate the effects of different selection mechanisms on the identified schemata and their frequencies.

Notes

Acknowledgements

The work described in this paper was done within the COMET Project Heuristic Optimization in Production and Logistics (HOPL), #843532 funded by the Austrian Research Promotion Agency (FFG).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Bogdan Burlacu
    • 1
    • 2
    Email author
  • Michael Affenzeller
    • 1
    • 2
  • Michael Kommenda
    • 1
    • 2
  • Gabriel Kronberger
    • 3
  • Stephan Winkler
    • 1
    • 2
  1. 1.Heuristic and Evolutionary Algorithms LaboratoryUniversity of Applied Sciences Upper AustriaHagenbergAustria
  2. 2.Institute for Formal Models and VerificationJohannes Kepler UniversityLinzAustria
  3. 3.Heuristic and Evolutionary Algorithms LaboratoryUniversity of Applied Sciences Upper AustriaHagenbergAustria

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