Genetic Programming Bibliography entries for Bogdan Burlacu

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GP coauthors/coeditors: Michael Affenzeller, Stephan M Winkler, Gabriel Kronberger, Michael Kommenda, Stefan Wagner, Viktoria Dorfer, Sebastian Dorl, Gerhard Halmerbauer, Tilman Koenigswieser, Julia Vetter, Lukas Kammerer, Kaifeng Yang, Christopher C Crary, Wolfgang Banzhaf, Fabricio Olivetti de Franca, Marco Virgolin, 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, Jaan Kasak, Meera Machado, Casper Wilstrup, William La Cava, Lavinia Ferariu, Christian Haider, Florian Bachinger, Reinhard Holecek, Andreas Gebeshuber, Johannes Karder, Andreas Beham, Patryk Orzechowski, Ying Jin, Jason H Moore, Etienne Russeil, Konstantin Malanchev, Emille Ishida, Marion Leroux, Clement Michelin, Guillaume Moinard, Emmanuel Gangler, Mariia Semenkina, Philipp Fleck,

Genetic Programming Articles by Bogdan Burlacu

  1. Bogdan Burlacu and Kaifeng Yang and Michael Affenzeller. Population diversity and inheritance in genetic programming for symbolic regression. Natural Computing, 23(3):531-566, 2024. Special Issue: Selected papers from the 27th International Conference on DNA Computing and Molecular Programming. details

  2. C. Haider and F. O. de Franca and B. Burlacu and G. Kronberger. Shape-constrained multi-objective genetic programming for symbolic regression. Applied Soft Computing, 132:109855, 2023. details

  3. G. Kronberger and F. O. de Franca and B. Burlacu and C. Haider and M. Kommenda. Shape-constrained Symbolic Regression - Improving Extrapolation with Prior Knowledge. Evolutionary Computation, 30(1):75-98, 2022. details

  4. Michael Kommenda and Bogdan Burlacu and Gabriel Kronberger and Michael Affenzeller. Parameter identification for symbolic regression using nonlinear least squares. Genetic Programming and Evolvable Machines, 21(3):471-501, 2020. Special Issue on Integrating numerical optimization methods with genetic programming. details

  5. 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 Books by Bogdan Burlacu

Genetic Programming PhD doctoral thesis Bogdan Burlacu

Genetic Programming conference papers by Bogdan Burlacu

  1. Etienne Russeil and Fabricio Olivetti de Franca and Konstantin Malanchev and Bogdan Burlacu and Emille Ishida and Marion Leroux and Clement Michelin and Guillaume Moinard and Emmanuel Gangler. Multiview Symbolic 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 961-970, Melbourne, Australia, 2024. Association for Computing Machinery. details

  2. Bogdan Burlacu. Backend-agnostic Tree Evaluation for Genetic Programming. In Stefan Wagner and Michael Affenzeller editors, Open Source Software for Evolutionary Computation, pages 1649-1657, Melbourne, Australia, 2024. Association for Computing Machinery. details

  3. Christopher Crary and Bogdan Burlacu and Wolfgang Banzhaf. Enhancing the Computational Efficiency of Genetic Programming through Alternative Floating-Point Primitives. In Heike Trautmann and Tea Tusar and Penousal Machado and Thomas Baeck editors, 18th International Conference on Parallel Problem Solving from Nature, University of Applied Sciences Upper Austria, Hagenberg, Austria, 2024. Springer. details

  4. Bogdan Burlacu. Gradient-based Local Search in Symbolic Regression. In Wolfgang Banzhaf and Ting Hu and Alexander Lalejini and Stephan Winkler editors, Genetic Programming Theory and Practice XXI, University of Michigan, USA, 2024. details

  5. Christian Haider and Fabricio Olivetti de Franca and Bogdan Burlacu and Florian Bachinger and Gabriel Kronberger and Michael Affenzeller. Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation. In Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu editors, Genetic Programming Theory and Practice XX, pages 225-240, Michigan State University, USA, 2023. Springer. details

  6. Christian Haider and Fabricio De Franca and Gabriel Kronberger and Bogdan Burlacu. Comparing Optimistic and Pessimistic Constraint Evaluation in Shape-constrained 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 938-945, Boston, USA, 2022. Association for Computing Machinery. details

  7. Bogdan Burlacu and Michael Kommenda and Gabriel Kronberger and Stephan M. Winkler and Michael Affenzeller. Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data. In Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf editors, Genetic Programming Theory and Practice XIX, pages 1-30, Ann Arbor, USA, 2022. Springer. details

  8. 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

  9. Bogdan Burlacu and Gabriel Kronberger and Michael Kommenda. Operon C++: An Efficient Genetic Programming Framework for Symbolic Regression. 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 1562-1570, internet, 2020. Association for Computing Machinery. details

  10. Mariia Semenkina and Bogdan Burlacu and Michael Affenzeller. Genetic Programming Based Evolvement of Models of Models. In Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia editors, International Conference on Computer Aided Systems Theory, EUROCAST 2019, volume 12013, pages 387-395, Las Palmas de Gran Canaria, Spain, 2019. Springer. details

  11. Lukas Kammerer and Gabriel Kronberger and Bogdan Burlacu and Stephan M. Winkler and Michael Kommenda and Michael Affenzeller. Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication. In Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel editors, Genetic Programming Theory and Practice XVII, pages 79-99, East Lansing, MI, USA, 2019. Springer. details

  12. Michael Affenzeller and Bogdan Burlacu and Viktoria Dorfer and Sebastian Dorl and Gerhard Halmerbauer and Tilman Koenigswieser and Michael Kommenda and Julia Vetter and Stephan M. Winkler. White Box vs. Black Box Modeling: On the Performance of Deep Learning, Random Forests, and Symbolic Regression in Solving Regression Problems. In Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia editors, 17th International Conference, Computer Aided Systems Theory - EUROCAST 2019, volume 12013, pages 288-295, Las Palmas de Gran Canaria, Spain, 2019. Springer. Revised Selected Papers, Part I. details

  13. Bogdan Burlacu and Gabriel Kronberger and Michael Kommenda and Michael Affenzeller. Parsimony measures in multi-objective genetic programming for symbolic regression. 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 338-339, Prague, Czech Republic, 2019. ACM. details

  14. Bogdan Burlacu and Lukas Kammerer and Michael Affenzeller and Gabriel Kronberger. Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression. In Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia editors, International Conference on Computer Aided Systems Theory, EUROCAST 2019, volume 12013, pages 361-369, Las Palmas de Gran Canaria, Spain, 2019. Springer. details

  15. B. Burlacu and M. Affenzeller and G. Kronberger and M. Kommenda. Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure. In 2019 IEEE Congress on Evolutionary Computation (CEC), pages 2175-2182, 2019. details

  16. Gabriel Kronberger and Lukas Kammerer and Bogdan Burlacu and Stephan M. Winkler and Michael Kommenda and Michael Affenzeller. Cluster Analysis of a Symbolic Regression Search Space. In Wolfgang Banzhaf and Lee Spector and Leigh Sheneman editors, Genetic Programming Theory and Practice XVI, pages 85-102, Ann Arbor, USA, 2018. Springer. details

  17. Bogdan Burlacu and Michael Affenzeller. Schema-based diversification in genetic programming. 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 1111-1118, Kyoto, Japan, 2018. ACM. details

  18. Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Gabriel Kronberger and Stephan Winkler. Schema Analysis in Tree-Based Genetic Programming. In Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo editors, Genetic Programming Theory and Practice XV, pages 17-37, University of Michigan in Ann Arbor, USA, 2017. Springer. details

  19. Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Gabriel Kronberger and Stephan M. Winkler. Analysis of Schema Frequencies in Genetic Programming. In Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia editors, 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017, Part I, volume 10671, pages 432-438, Las Palmas de Gran Canaria, Spain, 2017. Springer. Revised Selected Papers. details

  20. Michael Affenzeller and Stephan M. Winkler and Bogdan Burlacu and Gabriel Kronberger and Michael Kommenda and Stefan Wagner. Dynamic Observation of Genotypic and Phenotypic Diversity for Different Symbolic Regression GP Variants. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 1553-1558, Berlin, Germany, 2017. ACM. details

  21. Gabriel K. Kronberger and Bogdan Burlacu and Michael Kommenda and Stephan Winkler and Michael Affenzeller. Measures for the Evaluation and Comparison of Graphical Model Structures. In Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia editors, Computer Aided Systems Theory, EUROCAST 2017, volume 10671, pages 283-290, Las Palmas de Gran Canaria, Spain, 2017. details

  22. Michael Kommenda and Johannes Karder and Andreas Beham and Bogdan Burlacu and Gabriel K. Kronberger and Stefan Wagner and Michael Affenzeller. Optimization Networks for Integrated Machine Learning. In Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia editors, Computer Aided Systems Theory, EUROCAST 2017, volume 10671, pages 392-399, Las Palmas de Gran Canaria, Spain, 2017. details

  23. Michael Affenzeller and Bogdan Burlacu and Stephan M. Winkler and Michael Kommenda and Gabriel K. Kronberger and Stefan Wagner. Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals. In Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia editors, 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017, volume 10671, pages 424-431, Las Palmas de Gran Canaria, Spain, 2017. Springer. details

  24. Stephan M. Winkler and Michael Affenzeller and Bogdan Burlacu and Gabriel Kronberger and Michael Kommenda and Philipp Fleck. Similarity-based Analysis of Population Dynamics in Genetic Programming Performing Symbolic Regression. In Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier editors, Genetic Programming Theory and Practice XIV, pages 1-17, Ann Arbor, USA, 2016. Springer. details

  25. Michael Kommenda and Gabriel Kronberger and Michael Affenzeller and Stephan Winkler and Bogdan Burlacu. Evolving Simple Symbolic Regression Models by Multi-objective Genetic Programming. In Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon editors, Genetic Programming Theory and Practice XIII, pages 1-19, Ann Arbor, USA, 2015. Springer. details

  26. Bogdan Burlacu and Michael Affenzeller and Michael Kommenda. On the Effectiveness of Genetic Operations in Symbolic Regression. In Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia editors, 15th International Conference Computer Aided Systems Theory, EUROCAST 2015, volume 9520, pages 367-374, Las Palmas de Gran Canaria, Spain, 2015. Springer. Revised Selected Papers. details

  27. Bogdan Burlacu and Michael Kommenda and Michael Affenzeller. Building Blocks Identification Based on Subtree Sample Counts for Genetic Programming. In 2015 Asia-Pacific Conference on Computer Aided System Engineering (APCASE), pages 152-157, 2015. details

  28. Michael Kommenda and Bogdan Burlacu and Reinhard Holecek and Andreas Gebeshuber and Michael Affenzeller. Heat Treatment Process Parameter Estimation using Heuristic Optimization Algorithms. In Proceedings of the 27th European Modeling and Simulation Symposium EMSS 2015, pages 222-228, Bergeggi, Italy, 2015. details

  29. Stephan M. Winkler and Michael Affenzeller and Gabriel Kronberger and Michael Kommenda and Bogdan Burlacu and Stefan Wagner. Sliding Window Symbolic Regression for Detecting Changes of System Dynamics. In Rick Riolo and William P. Worzel and Mark Kotanchek editors, Genetic Programming Theory and Practice XII, pages 91-107, Ann Arbor, USA, 2014. Springer. details

  30. Michael Kommenda and Michael Affenzeller and Bogdan Burlacu and Gabriel Kronberger and Stephan M. Winkler. Genetic programming with data migration for symbolic regression. In Steven Gustafson and Ekaterina Vladislavleva editors, GECCO 2014 Workshop on Symbolic Regression and Modelling, pages 1361-1366, Vancouver, BC, Canada, 2014. ACM. details

  31. Bogdan Burlacu and Michael Affenzeller and Michael Kommenda. On the Evolutionary Behavior of Genetic Programming with Constants Optimization. In Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia editors, Computer Aided Systems Theory, EUROCAST 2013, volume 8111, pages 284-291, Las Palmas de Gran Canaria, Spain, 2013. Springer. 14th International Conference, Revised Selected Papers. details

  32. Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Stephan Winkler and Gabriel Kronberger. Visualization of genetic lineages and inheritance information in genetic programming. In Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks editors, GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion, pages 1351-1358, Amsterdam, The Netherlands, 2013. ACM. details

  33. Bogdan Burlacu and Michael Affenzeller and Michael Kommenda and Stephan M. Winkler and Gabriel Kronberger. Evolution Tracking in Genetic Programming. In Emilio Jimenez and Boris Sokolov editors, The 24th European Modeling and Simulation Symposium, EMSS 2012, Vienna, Austria, 2012. details

  34. Lavinia Ferariu and Bogdan Burlacu. Multiobjective genetic programming with adaptive clustering. In IEEE International Conference on Intelligent Computer Communication and Processing (ICCP 2011), pages 27-32, Cluj-Napoca, Romania, 2011. details

  35. Lavinia Ferariu and Bogdan Burlacu. Multiobjective Graph Genetic Programming with Encapsulation Applied to Neural System Identification. In 15th International Conference on System Theory, Control, and Computing (ICSTCC 2011), Sinaia, 2011. details

  36. Lavinia Ferariu and Bogdan Burlacu. Multiobjective design of evolutionary hybrid neural networks. In 17th International Conference on Automation and Computing (ICAC 2011), pages 195-200, Huddersfield, UK, 2011. details

  37. L. Ferariu and B. Burlacu. Graph genetic programming for hybrid neural networks design. In International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI), pages 547-552, 2010. details

Genetic Programming book chapters by Bogdan Burlacu

Genetic Programming other entries for Bogdan Burlacu