Genetic Programming Bibliography entries for Marco Virgolin

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GP coauthors/coeditors: Alberto Bartoli, Andrea De Lorenzo, Eric Medvet, Fabiano Tarlao, Fabricio Olivetti de Franca, 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, Hengzhe Zhang, Grant Dick, Alessandro Simon, Bogdan Burlacu, Jaan Kasak, Meera Machado, Casper Wilstrup, William La Cava, Joe Harrison, Tanja Alderliesten, Peter A N Bosman, Patryk Orzechowski, Ying Jin, Jason H Moore, Dazhuang Liu, Mauro Castelli, Ivo Goncalves, Tea Tusar, Giorgia Nadizar, Luigi Rovito, Thomas Uriot, Cees Witteveen, Arjan Bel, Francesca Randone, Ziyuan Wang, B V Balgobind, I W E M van Dijk, Jan Wiersma, P S Kroon, Geert O Janssens, M van Herk, David C Hodgson, Lorna Zadravec Zaletel, C R N Rasch,

Genetic Programming Articles by Marco Virgolin

  1. Giorgia Nadizar and Luigi Rovito and Andrea De Lorenzo and Eric Medvet and Marco Virgolin. An Analysis of the Ingredients for Learning Interpretable Symbolic Regression Models with Human-in-the-loop and Genetic Programming. ACM Transactions on Evolutionary Learning and Optimization, 4(1) 2024. 17000th GP entry. details

  2. M. Virgolin and T. Alderliesten and C. Witteveen and P. A. N. Bosman. Improving Model-based Genetic Programming for Symbolic Regression of Small Expressions. Evolutionary Computation, 29(2):211-237, 2021. details

  3. M Virgolin and Ziyuan Wang and B V Balgobind and I W E M van Dijk and J Wiersma and P S Kroon and G O Janssens and M van Herk and D C Hodgson and L Zadravec Zaletel and C R N Rasch and A Bel and P A N Bosman and T Alderliesten. Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy. Physics in Medicine \& Biology, 65(24):245021, 2020. details

  4. Marco Virgolin and Ziyuan Wang and Tanja Alderliesten and Peter A. N. Bosman. Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction. Journal of Medical Imaging, 7(4):046501, 2020. Winner Silver HUMIES. details

  5. Marco Virgolin and Tanja Alderliesten and Peter A. N. Bosman. On explaining machine learning models by evolving crucial and compact features. Swarm and Evolutionary Computation, 53:100640, 2020. details

  6. Eric Medvet and Marco Virgolin and Mauro Castelli and Peter A. N. Bosman and Ivo Goncalves and Tea Tusar. Unveiling evolutionary algorithm representation with DU maps. Genetic Programming and Evolvable Machines, 19(3):351-389, 2018. Special issue on genetic programming, evolutionary computation and visualization. details

  7. 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 PhD doctoral thesis Marco Virgolin

Genetic Programming conference papers by Marco Virgolin

  1. Joe Harrison and Marco Virgolin and Tanja Alderliesten and Peter Bosman. Mini-Batching, Gradient-Clipping, First- versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic 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 1127-1136, Lisbon, Portugal, 2023. Association for Computing Machinery. details

  2. Thomas Uriot and Marco Virgolin and Tanja Alderliesten and Peter Bosman. On genetic programming representations and fitness functions for interpretable dimensionality reduction. In Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto editors, Proceedings of the 2022 Genetic and Evolutionary Computation Conference, pages 458-466, Boston, USA, 2022. Association for Computing Machinery. details

  3. Dazhuang Liu and Marco Virgolin and Tanja Alderliesten and Peter Bosman. Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression. In Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto editors, Proceedings of the 2022 Genetic and Evolutionary Computation Conference, pages 973-981, Boston, USA, 2022. Association for Computing Machinery. Best Paper GP Track. details

  4. Marco Virgolin. Genetic Programming is Naturally Suited to Evolve Bagging Ensembles. 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, pages 830-839, internet, 2021. Association for Computing Machinery. details

  5. William La Cava and Patryk Orzechowski and Bogdan Burlacu and Fabricio de Franca and Marco Virgolin and Ying Jin and Michael Kommenda and Jason Moore. Contemporary Symbolic Regression Methods and their Relative Performance. In Joaquin Vanschoren and Sai-Kit Yeung editors, Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, volume 1, 2021. Curran. details

  6. Marco Virgolin and Andrea De Lorenzo and Eric Medvet and Francesca Randone. Learning a Formula of Interpretability to Learn Interpretable Formulas. In Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann editors, 16th International Conference on Parallel Problem Solving from Nature, Part II, volume 12270, pages 79-93, Leiden, Holland, 2020. Springer. details

  7. Marco Virgolin and Ziyuan Wang and Tanja Alderliesten and Peter A. N. Bosman. Machine learning for automatic construction of pediatric abdominal phantoms for radiation dose reconstruction. In P-H. Chen and T. M. Deserno editors, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, volume 11318, 2020. details

  8. Marco Virgolin and Tanja Alderliesten and Peter A. N. Bosman. Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression. In Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie Peprez Caceres and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre editors, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1084-1092, Prague, Czech Republic, 2019. ACM. details

  9. Marco Virgolin and Tanja Alderliesten and Arjan Bel and Cees Witteveen and Peter A. N. Bosman. Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors. In Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre editors, GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1395-1402, Kyoto, Japan, 2018. ACM. details

  10. Marco Virgolin and Tanja Alderliesten and Cees Witteveen and Peter A. N. Bosman. Scalable Genetic Programming by Gene-pool Optimal Mixing and Input-space Entropy-based Building-block Learning. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 1041-1048, Berlin, Germany, 2017. ACM. details

  11. Alberto Bartoli and Andrea De Lorenzo and Eric Medvet and Fabiano Tarlao and Marco Virgolin. Evolutionary Learning of Syntax Patterns for Genic Interaction Extraction. In Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco Fernandez de Vega and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr editors, GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pages 1183-1190, Madrid, Spain, 2015. ACM. details

Genetic Programming other entries for Marco Virgolin

  1. 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. Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. 2023. v3. details

  2. Dazhuang Liu and Marco Virgolin and Tanja Alderliesten and Peter A. N. Bosman. Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression. 2022. details

  3. Marco Virgolin and Eric Medvet and Tanja Alderliesten and Peter A. N. Bosman. Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning. 2022. details

  4. Thomas Uriot and Marco Virgolin and Tanja Alderliesten and Peter A. N. Bosman. On genetic programming representations and fitness functions for interpretable dimensionality reduction. 2022. details

  5. Marco Virgolin. Simple Simultaneous Ensemble Learning in Genetic Programming. 2020. details

  6. Marco Virgolin and Tanja Alderliesten and Cees Witteveen and Peter A. N. Bosman. A Model-based Genetic Programming Approach for Symbolic Regression of Small Expressions. 2019. details