Genetic Programming Bibliography entries for Ivo Goncalves

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GP coauthors/coeditors: Mauro Castelli, Luca Manzoni, Leonardo Vanneschi, Leonardo Trujillo, Sara Silva, Ales Popovic, Joana B Melo, Joao Manuel de Brito Carreiras, Carlos M Fonseca, Marta Seca, Carlos Antonio Goribar Jimenez, Yazmin Maldonado Robles, Paulo Lapa, Leonardo Rundo, Alberto Bartoli, Eric Medvet, Marco Virgolin, Peter A N Bosman, Tea Tusar, Frederico J J B Santos, Vijay Ingalalli, Susana de Almeida Mendes Vinga Martins, Jose Miguel Ranhada Vellez Caldas, Krzysztof Krawiec, Alberto Moraglio,

Genetic Programming Articles by Ivo Goncalves

  1. Frederico J. J. B. Santos and Ivo Goncalves and Mauro Castelli. Neuroevolution with box mutation: An adaptive and modular framework for evolving deep neural networks. Applied Soft Computing, 147:110767, 2023. details

  2. Luca Manzoni and Alberto Bartoli and Mauro Castelli and Ivo Goncalves and Eric Medvet. Specializing Context-Free Grammars With a (1 + 1)-EA. IEEE Transactions on Evolutionary Computation, 24(5):960-973, 2020. details

  3. 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

  4. Mauro Castelli and Ivo Goncalves and Leonardo Trujillo and Ales Popovic. An evolutionary system for ozone concentration forecasting. Information Systems Frontiers, 19(5):1123-1132, 2017. details

  5. Mauro Castelli and Leonardo Trujillo and Ivo Goncalves and Ales Popovic. An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming. Computers and Concrete, 19(6):651-658, 2017. details

Genetic Programming PhD doctoral thesis Ivo Goncalves

Genetic Programming conference papers by Ivo Goncalves

  1. Paulo Lapa and Ivo Goncalves and Leonardo Rundo and Mauro Castelli. Semantic learning machine improves the CNN-Based detection of prostate cancer in non-contrast-enhanced MRI. In Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu Ceberio Uribe and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier Del Ser and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William La Cava and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz 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 Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale editors, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 1837-1845, Prague, Czech Republic, 2019. ACM. details

  2. Paulo Lapa and Ivo Goncalves and Leonardo Rundo and Mauro Castelli. Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images: a study with the semantic learning machine. In Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu Ceberio Uribe and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier Del Ser and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William La Cava and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz 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 Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale editors, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 381-382, Prague, Czech Republic, 2019. ACM. details

  3. Ivo Goncalves and Marta Seca and Mauro Castelli. Explorations of the Semantic Learning Machine Neuroevolution Algorithm: Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives. In Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel editors, Genetic Programming Theory and Practice XVII, pages 39-62, East Lansing, MI, USA, 2019. Springer. details

  4. Mauro Castelli and Ivo Goncalves and Luca Manzoni and Leonardo Vanneschi. Pruning Techniques for Mixed Ensembles of Genetic Programming Models. In Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez editors, EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming, volume 10781, pages 52-67, Parma, Italy, 2018. Springer Verlag. details

  5. Leonardo Vanneschi and Mauro Castelli and Ivo Goncalves and Luca Manzoni and Sara Silva. Geometric semantic genetic programming for biomedical applications: A state of the art upgrade. In Jose A. Lozano editor, 2017 IEEE Congress on Evolutionary Computation (CEC), pages 177-184, Donostia, San Sebastian, Spain, 2017. IEEE. details

  6. Carlos Goribar-Jimenez and Yazmin Maldonado and Leonardo Trujillo and Mauro Castelli and Ivo Goncalves and Leonardo Vanneschi. Towards the development of a complete GP system on an FPGA using geometric semantic operators. In Jose A. Lozano editor, 2017 IEEE Congress on Evolutionary Computation (CEC), pages 1932-1939, Donostia, San Sebastian, Spain, 2017. IEEE. details

  7. Leonardo Vanneschi and Mauro Castelli and Luca Manzoni and Krzysztof Krawiec and Alberto Moraglio and Sara Silva and Ivo Goncalves. PSXO: Population-wide Semantic Crossover. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 257-258, Berlin, Germany, 2017. ACM. details

  8. Ivo Goncalves and Sara Silva and Carlos M. Fonseca and Mauro Castelli. Unsure when to Stop?: Ask Your Semantic Neighbors. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 929-936, Berlin, Germany, 2017. ACM. details

  9. Mauro Castelli and Luca Manzoni and Ivo Goncalves and Leonardo Vanneschi and Leonardo Trujillo and Sara Silva. An Analysis of Geometric Semantic Crossover: A Computational Geometry Approach. In Proceedings of the 8th International Joint Conference on Computational Intelligence, IJCCI (ECTA) 2016, pages 201-208, 2016. Scitepress. details

  10. Ivo Goncalves and Sara Silva and Carlos M. Fonseca and Mauro Castelli. Arbitrarily Close Alignments in the Error Space: a Geometric Semantic Genetic Programming Approach. In Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing editors, GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation, pages 99-100, Denver, USA, 2016. ACM. details

  11. Ivo Goncalves and Sara Silva and Carlos M. Fonseca. Semantic Learning Machine: A Feedforward Neural Network Construction Algorithm Inspired by Geometric Semantic Genetic Programming. In Francisco C. Pereira and Penousal Machado and Ernesto Costa and Amilcar Cardoso editors, Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, volume 9273, pages 280-285, Coimbra, Portugal, 2015. Springer. details

  12. Ivo Goncalves and Sara Silva and Carlos M. Fonseca. On the Generalization Ability of Geometric Semantic Genetic Programming. In Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim editors, 18th European Conference on Genetic Programming, volume 9025, pages 41-52, Copenhagen, 2015. Springer. details

  13. Ivo Goncalves and Sara Silva. Balancing Learning and Overfitting in Genetic Programming with Interleaved Sampling of Training data. In Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu editors, Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013, volume 7831, pages 73-84, Vienna, Austria, 2013. Springer Verlag. details

  14. Sara Silva and Vijay Ingalalli and Susana Vinga and Joao M. B. Carreiras and Joana B. Melo and Mauro Castelli and Leonardo Vanneschi and Ivo Goncalves and Jose Caldas. Prediction of Forest Aboveground Biomass: An Exercise on Avoiding Overfitting. In Anna I. Esparcia-Alcazar and Antonio Della Cioppa and Ivanoe De Falco and Ernesto Tarantino and Carlos Cotta and Robert Schaefer and Konrad Diwold and Kyrre Glette and Andrea Tettamanzi and Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and Aniko Ekart and Francisco Fernandez de Vega and Sara Silva and Evert Haasdijk and Gusz Eiben and Anabela Simoes and Philipp Rohlfshagen editors, Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC, volume 7835, pages 407-417, Vienna, 2013. Springer Verlag. details

  15. Ivo Goncalves and Sara Silva and Joana B. Melo and Joao M. B. Carreiras. Random Sampling Technique for Overfitting Control in Genetic Programming. In Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta editors, Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012, volume 7244, pages 218-229, Malaga, Spain, 2012. Springer Verlag. details

  16. Ivo Goncalves and Sara Silva. Experiments on Controlling Overfitting in Genetic Programming. In Local proceedings of the 15th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence, pages 152-166, 2011. details