Genetic Programming Bibliography entries for Lukas Kammerer

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GP coauthors/coeditors: Bogdan Burlacu, Michael Affenzeller, Gabriel Kronberger, Michael Kommenda, Stephan M Winkler, Fabricio Olivetti de Franca, Harry Desmond, Deaglan J Bartlett,

Genetic Programming conference papers by Lukas Kammerer

  1. Gabriel Kronberger and Fabricio Olivetti de Franca and Harry Desmond and Deaglan Bartlett and Lukas Kammerer. The Inefficiency of Genetic Programming for Symbolic Regression. In Heike Trautmann and Tea Tusar and Penousal Machado and Thomas Baeck editors, 18th International Conference on Parallel Problem Solving from Nature, University of Applied Sciences Upper Austria, Hagenberg, Austria, 2024. Springer. details

  2. Lukas Kammerer and Gabriel Kronberger and Michael Kommenda. Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization. In Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia editors, 18th International Conference on Computer Aided Systems Theory, EUROCAST 2022, volume 13789, pages 139-146, Las Palmas de Gran Canaria, Spain, 2022. Springer. Revised Selected Papers. details

  3. Lukas Kammerer and Gabriel Kronberger and Stephan Winkler. Empirical Analysis of Variance for Genetic Programming based Symbolic Regression. In Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton editors, Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion, pages 251-252, internet, 2021. Association for Computing Machinery. details

  4. Gabriel Kronberger and Lukas Kammerer and Michael Kommenda. Identification of Dynamical Systems Using Symbolic Regression. In Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia editors, International Conference on Computer Aided Systems Theory, EUROCAST 2019, volume 12013, pages 370-377, Las Palmas de Gran Canaria, Spain, 2019. Springer. details

  5. Lukas Kammerer and Gabriel Kronberger and Bogdan Burlacu and Stephan M. Winkler and Michael Kommenda and Michael Affenzeller. Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication. In Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel editors, Genetic Programming Theory and Practice XVII, pages 79-99, East Lansing, MI, USA, 2019. Springer. details

  6. Lukas Kammerer and Gabriel Kronberger and Michael Kommenda. Data Aggregation for Reducing Training Data in Symbolic Regression. In Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia editors, International Conference on Computer Aided Systems Theory, EUROCAST 2019, volume 12013, pages 378-386, Las Palmas de Gran Canaria, Spain, 2019. Springer. details

  7. Bogdan Burlacu and Lukas Kammerer and Michael Affenzeller and Gabriel Kronberger. Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression. In Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia editors, International Conference on Computer Aided Systems Theory, EUROCAST 2019, volume 12013, pages 361-369, Las Palmas de Gran Canaria, Spain, 2019. Springer. details

  8. Gabriel Kronberger and Lukas Kammerer and Bogdan Burlacu and Stephan M. Winkler and Michael Kommenda and Michael Affenzeller. Cluster Analysis of a Symbolic Regression Search Space. In Wolfgang Banzhaf and Lee Spector and Leigh Sheneman editors, Genetic Programming Theory and Practice XVI, pages 85-102, Ann Arbor, USA, 2018. Springer. details