Genetic Programming Bibliography entries for Ting Hu
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GP coauthors/coeditors:
Wolfgang Banzhaf,
Gabriela Ochoa,
Christian Darabos,
Mario Giacobini,
Jason H Moore,
Shengkai Geng,
Simon Harding,
Joshua L Payne,
Karoliina Oksanen,
Weidong Zhang,
Edward Randell,
Andrew Furey,
Guangju Zhai,
Marco Tomassini,
Lukas Sekanina,
Miguel Nicolau,
Nuno Lourenco,
Eric Medvet,
Stephan M Winkler,
Leonardo Trujillo,
Charles Ofria,
Krzysztof Krawiec,
Alberto Moraglio,
A Sima Etaner-Uyar,
Bin Hu,
Michael Y Lee,
Kyle L Nickerson,
Antonina Kolokolova,
Mengjie Zhang,
Jinting Zhang,
Yu Zhang2,
Xiaodong Liang,
Mohammad Zawad Ali,
Md Nasmus Sakib Khan Shabbir,
Yuanzhu Chen,
Ryan Zhou,
Christian Muise,
Genetic Programming Articles by Ting Hu
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Leonardo Trujillo and Ting Hu and Nuno Lourenco and Mengjie Zhang.
Editorial Introduction.
Genetic Programming and Evolvable Machines, 23(3):305-307, 2022.
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Ting Hu and Miguel Nicolau and Lukas Sekanina.
Special issue on highlights of genetic programming 2019 events.
Genetic Programming and Evolvable Machines, 21(3):283-285, 2020.
Guest Editorial.
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Ting Hu and Marco Tomassini and Wolfgang Banzhaf.
A network perspective on genotype-phenotype mapping in genetic programming.
Genetic Programming and Evolvable Machines, 21(3):375-397, 2020.
Special Issue: Highlights of Genetic Programming 2019 Events.
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Michael Y. Lee and Ting Hu.
Computational Methods for the Discovery of Metabolic Markers of Complex Traits.
Metabolites, 9(4) 2019.
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Ting Hu and Lukas Sekanina.
EuroGP 2019 Panel Discussion: What is the Killer Application of GP?.
SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, 12(2):3-7, 2019.
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Ting Hu and Wolfgang Banzhaf and Jason H. Moore.
The effects of recombination on phenotypic exploration and robustness in evolution.
Artificial Life, 20(4):457-470, 2014.
Ten thousandth GP entry in the genetic programming bibliography.
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Ting Hu and Joshua Payne and Wolfgang Banzhaf and Jason Moore.
Evolutionary dynamics on multiple scales: a quantitative analysis of the interplay between genotype, phenotype, and fitness in linear genetic programming.
Genetic Programming and Evolvable Machines, 13(3):305-337, 2012.
Special issue on selected papers from the 2011 European conference on genetic programming.
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Ting Hu and Wolfgang Banzhaf.
Evolvability and Speed of Evolutionary Algorithms in Light of Recent Developments in Biology.
Journal of Artificial Evolution and Applications, 2010:Article ID 568375, 2010.
Review Article.
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Ting Hu and Simon Harding and Wolfgang Banzhaf.
Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm.
Genetic Programming and Evolvable Machines, 11(2):205-225, 2010.
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Genetic Programming PhD doctoral thesis Ting Hu
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Ting Hu.
Evolvability and Rate of Evolution in Evolutionary Computation. PhD thesis,
Department of Computer Science, Memorial University of Newfoundland, ST. John's, Newfoundland, Canada, 2010.
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Genetic Programming Conference proceedings edited by Ting Hu
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Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu editors,
Genetic Programming Theory and Practice XX. Michigan State University, USA, Springer, 2023.
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Ting Hu and Nuno Lourenco and Eric Medvet editors,
EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming. Volume 12691 of LNCS, Virtual Event, Springer Verlag, 2021.
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Ting Hu and Nuno Lourenco and Eric Medvet editors,
EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming. Volume 12101 of LNCS, Seville, Spain, Springer Verlag, 2020.
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Lukas Sekanina and Ting Hu and Nuno Lourenco editors,
EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming. Volume 11451 of LNCS, Leipzig, Germany, Springer Verlag, 2019.
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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 of LNCS, Vienna, Austria, Springer, 2013.
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Genetic Programming conference papers by Ting Hu
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Wolfgang Banzhaf and Ting Hu.
Linear Genetic Programming. In
Mengjie Zhang and Emma Hart editors,
Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion, pages 759-771, Melbourne, Australia, 2024. Association for Computing Machinery.
Tutorial.
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Ting Hu and Gabriela Ochoa and Wolfgang Banzhaf.
Phenotype Search Trajectory Networks for Linear Genetic Programming. In
Gisele Pappa and Mario Giacobini and Zdenek Vasicek editors,
EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming, volume 13986, pages 52-67, Brno, Czech Republic, 2023. Springer Verlag.
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Wolfgang Banzhaf and Ting Hu and Gabriela Ochoa.
How the Combinatorics of Neutral Spaces Leads Genetic Programming to Discover Simple Solutions. In
Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu editors,
Genetic Programming Theory and Practice XX, pages 65-86, Michigan State University, USA, 2023. Springer.
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Jinting Zhang and Ting Hu.
Regulatory Genotype-to-Phenotype Mappings Improve Evolvability in Genetic Programming. In
Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco Fernandez de Vega and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio Della Cioppa and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William La Cava and Gabriel Kronberger and Steven Gustafson editors,
Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion, pages 623-626, Boston, USA, 2022. Association for Computing Machinery.
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Ryan Zhou and Christian Muise and Ting Hu.
Permutation-Invariant Representation of Neural Networks with Neuron Embeddings. In
Eric Medvet and Gisele Pappa and Bing Xue editors,
EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming, volume 13223, pages 294-308, Madrid, Spain, 2022. Springer Verlag.
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Kyle Nickerson and Antonina Kolokolova and Ting Hu.
Creating Diverse Ensembles for Classification with Genetic Programming and Neuro-MAP-Elites. In
Eric Medvet and Gisele Pappa and Bing Xue editors,
EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming, volume 13223, pages 212-227, Madrid, Spain, 2022. Springer Verlag.
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Ting Hu.
Genetic Programming for Interpretable and Explainable Machine Learning. In
Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf editors,
Genetic Programming Theory and Practice XIX, pages 81-90, Ann Arbor, USA, 2022. Springer.
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Kyle L. Nickerson and Ting Hu.
Principled quality diversity for ensemble classifiers using MAP-Elites. 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 Companion, pages 259-260, internet, 2021. Association for Computing Machinery.
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Yu Zhang2 and Yuanzhu Chen and Ting Hu.
Classification of Autism Genes using Network Science and Linear Genetic Programming. In
Ting Hu and Nuno Lourenco and Eric Medvet editors,
EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming, pages 279-294, Seville, Spain, 2020. Springer Verlag.
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Shengkai Geng and Ting Hu.
Sports Games Modeling and Prediction using Genetic Programming. In
Yaochu Jin editor,
2020 IEEE Congress on Evolutionary Computation, CEC 2020, page paper id24100, internet, 2020. IEEE Press.
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Yu Zhang2 and Ting Hu and Xiaodong Liang and Mohammad Zawad Ali and Md Nasmus Sakib Khan Shabbir.
Fault Detection and Classification for Induction Motors using Genetic Programming. In
Lukas Sekanina and Ting Hu and Nuno Lourenco editors,
EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming, volume 11451, pages 178-193, Leipzig, Germany, 2019. Springer Verlag.
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Ting Hu.
Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics?. In
Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel editors,
Genetic Programming Theory and Practice XVII, pages 63-77, East Lansing, MI, USA, 2019. Springer.
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Ting Hu and Marco Tomassini and Wolfgang Banzhaf.
Complex Network Analysis of a Genetic Programming Phenotype Network. In
Lukas Sekanina and Ting Hu and Nuno Lourenco editors,
EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming, volume 11451, pages 49-63, Leipzig, Germany, 2019. Springer Verlag.
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Ting Hu and Karoliina Oksanen and Weidong Zhang and Edward Randell and Andrew Furey and Guangju Zhai.
Analyzing Feature Importance for Metabolomics using Genetic Programming. 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 68-83, Parma, Italy, 2018. Springer Verlag.
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Karoliina Oksanen and Ting Hu.
Lexicase selection promotes effective search and behavioural diversity of solutions in Linear Genetic Programming. In
Jose A. Lozano editor,
2017 IEEE Congress on Evolutionary Computation (CEC), pages 169-176, Donostia, San Sebastian, Spain, 2017. IEEE.
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Ting Hu and Wolfgang Banzhaf.
Neutrality, Robustness, and Evolvability in Genetic Programming. In
Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier editors,
Genetic Programming Theory and Practice XIV, pages 101-117, Ann Arbor, USA, 2016. Springer.
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Ting Hu and Wolfgang Banzhaf.
Quantitative Analysis of Evolvability using Vertex Centralities in Phenotype Network. In
Tobias Friedrich editor,
GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation, pages 733-740, Denver, USA, 2016. ACM.
Nominated for best paper.
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Ting Hu and Wolfgang Banzhaf and Jason Moore.
Population Exploration on Genotype Networks in Genetic Programming. In
Thomas Bartz-Beielstein and Juergen Branke and Bogdan Filipic and Jim Smith editors,
13th International Conference on Parallel Problem Solving from Nature, volume 8672, pages 424-333, Ljubljana, Slovenia, 2014. Springer.
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Ting Hu and Wolfgang Banzhaf and Jason H. Moore.
Robustness and Evolvability of Recombination in Linear Genetic Programming. 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 97-108, Vienna, Austria, 2013. Springer Verlag.
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Christian Darabos and Mario Giacobini and Ting Hu and Jason H. Moore.
Levy-Flight Genetic Programming: Towards a New Mutation Paradigm. In
Mario Giacobini and Leonardo Vanneschi and William S. Bush editors,
10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012, volume 7246, pages 38-49, Malaga, Spain, 2012. Springer Verlag.
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Ting Hu and Joshua Payne and Jason Moore and Wolfgang Banzhaf.
Robustness, Evolvability, and Accessibility in Linear Genetic Programming. In
Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado editors,
Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011, volume 6621, pages 13-24, Turin, Italy, 2011. Springer Verlag.
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Ting Hu and Wolfgang Banzhaf.
The Role of Population Size in Rate of Evolution in Genetic Programming. In
Leonardo Vanneschi and Steven Gustafson and Alberto Moraglio and Ivanoe De Falco and Marc Ebner editors,
Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009, volume 5481, pages 85-96, Tuebingen, 2009. Springer.
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Ting Hu and Wolfgang Banzhaf.
Neutrality and variability: two sides of evolvability in linear genetic programming. In
Guenther Raidl and Franz Rothlauf and Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and Mauro Birattari and Clare Bates Congdon and Martin Middendorf and Christian Blum and Carlos Cotta and Peter Bosman and Joern Grahl and Joshua Knowles and David Corne and Hans-Georg Beyer and Ken Stanley and Julian F. Miller and Jano van Hemert and Tom Lenaerts and Marc Ebner and Jaume Bacardit and Michael O'Neill and Massimiliano Di Penta and Benjamin Doerr and Thomas Jansen and Riccardo Poli and Enrique Alba editors,
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pages 963-970, Montreal, 2009. ACM.
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Ting Hu and Wolfgang Banzhaf.
Measuring rate of evolution in genetic programming using amino acid to synonymous substitution ratio ka/ks. In
Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener editors,
GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 1337-1338, Atlanta, GA, USA, 2008. ACM.
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Ting Hu and Wolfgang Banzhaf.
Nonsynonymous to Synonymous Substitution Ratio ka/ks: Measurement for Rate of Evolution in Evolutionary Computation. In
Gunter Rudolph and Thomas Jansen and Simon Lucas and Carlo Poloni and Nicola Beume editors,
Parallel Problem Solving from Nature - PPSN X, volume 5199, pages 448-457, Dortmund, 2008. Springer.
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Genetic Programming book chapters by Ting Hu
Genetic Programming technical reports by Ting Hu
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Ting Hu and Wolfgang Banzhaf.
Evolvability and Acceleration in Evolutionary Computation. Technical report,
2008-04,
Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada A1B 3X5, 2008.
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Genetic Programming other entries for Ting Hu