Genetic Programming Bibliography entries for Hengzhe Zhang

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GP coauthors/coeditors: Fabricio Olivetti de Franca, Marco Virgolin, Michael Kommenda, Maimuna Majumder, Miles Cranmer, Guilherme Jorge Nunes Monteiro Espada, Leon Ingelse, Alcides Fonseca, Mikel Landajuela, Brenden Kyle Petersen, Ruben Glatt, T Nathan Mundhenk, Chak Shing Lee, Jacob Dean Hochhalter, David L Randall, Pierre-Alexandre Kamienny, Grant Dick, Alessandro Simon, Bogdan Burlacu, Jaan Kasak, Meera Machado, Casper Wilstrup, William La Cava, Tonglin Liu, Hu Zhang, Aimin Zhou, Xin Lin, Hong Qian, Qi Chen, Bing Xue, Mengjie Zhang, Alberto Tonda, Wolfgang Banzhaf, Evelyne Lutton, Yan Wang,

Genetic Programming Articles by Hengzhe Zhang

  1. Hengzhe Zhang and Aimin Zhou and Qi Chen and Bing Xue and Mengjie Zhang. SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method. IEEE Transactions on Evolutionary Computation, 28(5):1484-1498, 2024. details

  2. Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang. A geometric semantic macro-crossover operator for evolutionary feature construction in regression. Genetic Programming and Evolvable Machines, 25:Article number: 2, 2024. Online first. details

  3. Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang. A Semantic-Based Hoist Mutation Operator for Evolutionary Feature Construction in Regression. IEEE Transactions on Evolutionary Computation, 28(6):1689-1703, 2024. details

  4. Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang. Modular Multi-Tree Genetic Programming for Evolutionary Feature Construction for Regression. IEEE Transactions on Evolutionary Computation, 28(5):1455-1469, 2024. details

  5. Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang. MAP-Elites for Genetic Programming-Based Ensemble Learning: An Interactive Approach [AI-eXplained]. IEEE Computational Intelligence Magazine, 18(4):62-63, 2023. details

  6. Hengzhe Zhang and Aimin Zhou and Hong Qian and Hu Zhang. PS-Tree: A piecewise symbolic regression tree. Swarm and Evolutionary Computation, 71:101061, 2022. details

  7. Hengzhe Zhang and Aimin Zhou and Hu Zhang. An Evolutionary Forest for Regression. IEEE Transactions on Evolutionary Computation, 26(4):735-749, 2022. details

  8. Hengzhe Zhang and Aimin Zhou and Xin Lin. Interpretable policy derivation for reinforcement learning based on evolutionary feature synthesis. Complex \& Intelligent Systems, 6:741-753, 2020. details

  9. Tonglin Liu and Hengzhe Zhang and Hu Zhang and Aimin Zhou. Information Fusion in Offspring Generation: A Case Study in Gene Expression Programming. IEEE Access, 8:74782-74792, 2020. details

  10. F. O. de Franca and M. Virgolin and M. Kommenda and M. S. Majumder and M. Cranmer and G. Espada and L. Ingelse and A. Fonseca and M. Landajuela and B. Petersen and R. Glatt and N. Mundhenk and C. S. Lee and J. D. Hochhalter and D. L. Randall and P. Kamienny and H. Zhang and G. Dick and A. Simon and B. Burlacu and Jaan Kasak and Meera Machado and Casper Wilstrup and W. G. La Cava. SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation. IEEE Transactions on Evolutionary Computation. Early Access. details

Genetic Programming conference papers by Hengzhe Zhang

  1. Hengzhe Zhang and Alberto Tonda and Qi Chen and Bing Xue and Evelyne Lutton and Mengjie Zhang. Micro-Step Time-Series Regression: Insights from System Identification Using Symbolic Regression. In Bing Xue and Luca Manzoni and Illya Bakurov editors, European Conference on Genetic Programming, EuroGP 2025, volume 15609, pages 240-256, Trieste, 2025. Springer Nature. details

  2. Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang. A Semantic-based Hoist Mutation Operator for Evolutionary Feature Construction in Regression [Hot off the Press]. In Marcus Gallagher editor, Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion, pages 65-66, Melbourne, Australia, 2024. Association for Computing Machinery. details

  3. Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang. Bias-Variance Decomposition: An Effective Tool to Improve Generalization of Genetic Programming-based Evolutionary Feature Construction for Regression. In Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva editors, Proceedings of the 2024 Genetic and Evolutionary Computation Conference, pages 998-1006, Melbourne, Australia, 2024. Association for Computing Machinery. details

  4. Hengzhe Zhang and Qi Chen and Bing Xue and Mengjie Zhang and Wolfgang Banzhaf. P-Mixup: Improving Generalization Performance of Evolutionary Feature Construction with Pessimistic Vicinal Risk Minimization. In Heike Trautmann and Tea Tusar and Penousal Machado and Thomas Baeck editors, 18th International Conference on Parallel Problem Solving from Nature, volume 15148, pages 201-220, University of Applied Sciences Upper Austria, Hagenberg, Austria, 2024. Springer. details

  5. Hengzhe Zhang and Qi Chen and Bing Xue and Yan Wang and Aimin Zhou and Mengjie Zhang. EvoFeat: Genetic Programming-Based Feature Engineering Approach to Tabular Data Classification. In Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini editors, Genetic Programming Theory and Practice XXI, pages 27-49, University of Michigan, USA, 2024. Springer. details

  6. Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang. Improving Generalization of Evolutionary Feature Construction with Minimal Complexity Knee Points in Regression. In Mario Giacobini and Bing Xue and Luca Manzoni editors, EuroGP 2024: Proceedings of the 27th European Conference on Genetic Programming, volume 14631, pages 142-158, Aberystwyth, 2024. Springer. details

  7. Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang. Automatically Choosing Selection Operator Based on Semantic Information in Evolutionary Feature Construction. In Fenrong Liu and Arun Anand Sadanandan and Duc Nghia Pham and Petrus Mursanto and Dickson Lukose editors, Pacific Rim International Conference on Artificial Intelligence, volume 14326, pages 385-397, Jakarta, Indonesia, 2023. Springer Nature. details

  8. Hengzhe Zhang and Aimin Zhou and Qi Chen and Bing Xue and Mengjie Zhang. Genetic Programming-Based Evolutionary Feature Construction for Heterogeneous Ensemble Learning [Hot of the Press]. In Alberto Moraglio editor, Proceedings of the 2023 Genetic and Evolutionary Computation Conference, pages 49-50, Lisbon, Portugal, 2023. Association for Computing Machinery. details

  9. Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang. A Double Lexicase Selection Operator for Bloat Control in Evolutionary Feature Construction for Regression. In Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen editors, Proceedings of the 2023 Genetic and Evolutionary Computation Conference, pages 1194-1202, Lisbon, Portugal, 2023. Association for Computing Machinery. details

  10. Hengzhe Zhang and Qi Chen and Alberto Tonda and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang. MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning. In Gisele Pappa and Mario Giacobini and Zdenek Vasicek editors, EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming, volume 13986, pages 84-100, Brno, Czech Republic, 2023. Springer Verlag. details

  11. Hengzhe Zhang and Aimin Zhou. RL-GEP: Symbolic Regression via Gene Expression Programming and Reinforcement Learning. In 2021 International Joint Conference on Neural Networks, IJCNN, Shenzhen, China, 2021. IEEE. details

  12. Hu Zhang and Hengzhe Zhang and Aimin Zhou. A Multi-metric Selection Strategy for Evolutionary Symbolic Regression. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 585-591, 2020. details

Genetic Programming other entries for Hengzhe Zhang