Genetic Programming Bibliography entries for Roberto Santana

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GP coauthors/coeditors: Vidal Daniel da Fontoura, Aurora Trinidad Ramirez Pozo, Unai Garciarena Hualde, Alexander Mendiburu, Pedro Larranaga, Hossein Karshenas, Concha Bielza, Ricardo Henrique Remes de Lima, Dimmy Magalhaes, Ibai Roman, Jose A Lozano, Luis Marti, Mengjie Zhang,

Genetic Programming Articles by Roberto Santana

  1. Ricardo H. R. Lima and Dimmy Magalhaes and Aurora Pozo and Alexander Mendiburu and Roberto Santana. A grammar-based GP approach applied to the design of deep neural networks. Genetic Programming and Evolvable Machines, 23(3):427-452, 2022. Special Issue: Highlights of Genetic Programming 2021 Events. details

  2. Ibai Roman and Roberto Santana and Alexander Mendiburu and Jose A. Lozano. Evolving Gaussian process kernels from elementary mathematical expressions for time series extrapolation. Neurocomputing, 462:426-439, 2021. details

  3. Roberto Santana and Luis Marti and Mengjie Zhang. GP-based methods for domain adaptation: using brain decoding across subjects as a test-case. Genetic Programming and Evolvable Machines, 20(3):385-411, 2019. details

  4. Pedro Larranaga and Hossein Karshenas and Concha Bielza and Roberto Santana. A review on probabilistic graphical models in evolutionary computation. Journal of Heuristics, 18(5):795-819, 2012. details

Genetic Programming conference papers by Roberto Santana

  1. R. Santana. Semantic Composition of Word-Embeddings with Genetic Programming. In Heuristics for Optimization and Learning, 2021. Springer. details

  2. Ricardo Henrique Remes de Lima and Aurora Pozo and Alexander Mendiburu and Roberto Santana. Automatic Design of Deep Neural Networks Applied to Image Segmentation Problems. In Ting Hu and Nuno Lourenco and Eric Medvet editors, EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming, volume 12691, pages 98-113, Virtual Event, 2021. Springer Verlag. Best paper candidate. details

  3. Ricardo Henrique Remes Lima and Aurora Pozo and Alexander Mendiburu and Roberto Santana. A Symmetric grammar approach for designing segmentation models. In Yaochu Jin editor, 2020 IEEE Congress on Evolutionary Computation, CEC 2020, page paper id24460, internet, 2020. IEEE Press. details

  4. Ricardo Henrique Remes de Lima and Aurora T. R. Pozo and Roberto Santana. Automatic Design of Convolutional Neural Networks using Grammatical Evolution. In 8th Brazilian Conference on Intelligent Systems, BRACIS, pages 329-334, Salvador, Brazil, 2019. IEEE. details

  5. Ibai Roman and Alexander Mendiburu and Roberto Santana and Jose A. Lozano. Sentiment analysis with genetically evolved Gaussian kernels. 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 1328-1337, Prague, Czech Republic, 2019. ACM. details

  6. Unai Garciarena and Roberto Santana and Alexander Mendiburu. Analysis of the Complexity of the Automatic Pipeline Generation Problem. In 2018 IEEE Congress on Evolutionary Computation (CEC), 2018. details

  7. Vidal D. Fontoura and Aurora T. R. Pozo and Roberto Santana. Automated design of hyper-heuristics components to solve the PSP problem with HP model. In Jose A. Lozano editor, 2017 IEEE Congress on Evolutionary Computation (CEC), pages 1848-1855, Donostia, San Sebastian, Spain, 2017. IEEE. details

  8. Unai Garciarena and Roberto Santana. Evolutionary optimization of compiler flag selection by learning and exploiting flags interactions. In Justyna Petke and David R. White and Westley Weimer editors, Genetic Improvement 2016 Workshop, pages 1159-1166, Denver, 2016. ACM. details

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