Genetic Programming Bibliography entries for Joerg Haehner

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GP coauthors/coeditors: Henning Cui, Andreas Margraf, David Paetzel, Michael Heider, Anthony Stein, Leonhard Engstler, Steffen Geinitz, Stefan Baumann,

Genetic Programming conference papers by Joerg Haehner

  1. Andreas Margraf and Henning Cui and Stefan Baumann and Joerg Haehner. Filter Evolution Using Cartesian Genetic Programming for Time Series Anomaly Detection. In Niki van Stein and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Joaquim Filipe editors, Proceedings of the 15th International Joint Conference on Computational Intelligence, IJCCI 2023, Rome, Italy, November 13-15, 2023, pages 300-307, 2023. SCITEPRESS. details

  2. Henning Cui and Andreas Margraf and Michael Heider and Joerg Haehner. Towards Understanding Crossover for Cartesian Genetic Programming. In Niki van Stein and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Joaquim Filipe editors, Proceedings of the 15th International Joint Conference on Computational Intelligence, IJCCI 2023, Rome, Italy, November 13-15, 2023, pages 308-314, 2023. SCITEPRESS. details

  3. Henning Cui and Andreas Margraf and Joerg Haehner. Equidistant Reorder Operator for Cartesian Genetic Programming. In Niki van Stein and Francesco Marcelloni and H. K. Lam and Marie Cottrell and Joaquim Filipe editors, Proceedings of the 15th International Joint Conference on Computational Intelligence, IJCCI 2023, Rome, Italy, November 13-15, 2023, pages 64-74, 2023. SCITEPRESS. details

  4. Henning Cui and David Paetzel and Andreas Margraf and Joerg Haehner. Weighted Mutation of Connections To Mitigate Search Space Limitations in Cartesian Genetic Programming. In Francisco Chicano and Franz Rothlauf editors, Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, pages 50-60, Potsdam, Germany, 2023. Association for Computing Machinery. details

  5. Henning Cui and Andreas Margraf and Joerg Haehner. Refining Mutation Variants in Cartesian Genetic Programming. In Marjan Mernik and Tome Eftimov and Matej Crepinsek editors, Bioinspired Optimization Methods and Their Applications, volume 13627, pages 185-200, 2022. Springer. details

  6. Andreas Margraf and Anthony Stein and Leonhard Engstler and Steffen Geinitz and Joerg Haehner. An Evolutionary Learning Approach to Self-configuring Image Pipelines in the Context of Carbon Fiber Fault Detection. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 147-154, 2017. details