Genetic Programming Bibliography entries for Stefano Ruberto

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GP coauthors/coeditors: Mauro Castelli, Leonardo Vanneschi, Sara Silva, Elisabetta Manduchi, Weixuan Fu, Joseph D Romano, Jason H Moore, Valerio Terragni,

Genetic Programming Articles by Stefano Ruberto

Genetic Programming PhD doctoral thesis Stefano Ruberto

Genetic Programming conference papers by Stefano Ruberto

  1. Stefano Ruberto and Valerio Terragni and Jason Moore. Towards Effective GP Multi-Class Classification Based on Dynamic Targets. 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 812-821, internet, 2021. Association for Computing Machinery. Nominated for best paper. details

  2. Stefano Ruberto and Valerio Terragni and Jason H. Moore. Image Feature Learning with Genetic Programming. 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 63-78, Leiden, Holland, 2020. Springer. details

  3. Stefano Ruberto and Valerio Terragni and Jason H. Moore. Image Feature Learning with a Genetic Programming Autoencoder. In Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William La Cava and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe De Falco and Antonio Della Cioppa and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik editors, Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pages 245-246, internet, 2020. Association for Computing Machinery. details

  4. Stefano Ruberto and Valerio Terragni and Jason H. Moore. SGP-DT: Towards Effective Symbolic Regression with a Semantic GP Approach Based on Dynamic Targets. In Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William La Cava and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe De Falco and Antonio Della Cioppa and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik editors, Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pages 25-26, internet, 2020. Association for Computing Machinery. details

  5. Stefano Ruberto and Valerio Terragni and Jason H. Moore. SGP-DT: Semantic Genetic Programming Based on Dynamic Targets. In Ting Hu and Nuno Lourenco and Eric Medvet editors, EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming, volume 12101, pages 167-183, Seville, Spain, 2020. Springer Verlag. details

  6. Stefano Ruberto and Leonardo Vanneschi and Mauro Castelli and Sara Silva. ESAGP -- A Semantic GP Framework Based on Alignment in the Error Space. In Miguel Nicolau and Krzysztof Krawiec and Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and Juan J. Merelo and Victor M. Rivas Santos and Kevin Sim editors, 17th European Conference on Genetic Programming, volume 8599, pages 150-161, Granada, Spain, 2014. Springer. details

  7. Mauro Castelli and Leonardo Vanneschi and Sara Silva and Stefano Ruberto. How to Exploit Alignment in the Error Space: Two Different GP Models. In Rick Riolo and William P. Worzel and Mark Kotanchek editors, Genetic Programming Theory and Practice XII, pages 133-148, Ann Arbor, USA, 2014. Springer. details