Search

Your search keyword '"Silva, Sara"' showing total 111 results
Start Over Author "Silva, Sara" Topic genetic programming
111 results on '"Silva, Sara"'

3. Feature Selection on Epistatic Problems Using Genetic Algorithms with Nested Classifiers

Author

Carvalho, Pedro, Ribeiro, Bruno, Rodrigues, Nuno M., Batista, João E., Vanneschi, Leonardo, Silva, Sara, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Correia, João, editor, Smith, Stephen, editor, and Qaddoura, Raneem, editor

Published
2023
Full Text
View/download PDF

4. SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming

Author

Rodrigues, Nuno M., Batista, João E., La Cava, William, Vanneschi, Leonardo, Silva, Sara, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Medvet, Eric, editor, Pappa, Gisele, editor, and Xue, Bing, editor

Published
2022
Full Text
View/download PDF

5. The Usability Argument for Refinement Typed Genetic Programming

Author

Fonseca, Alcides, Santos, Paulo, Silva, Sara, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bäck, Thomas, editor, Preuss, Mike, editor, Deutz, André, editor, Wang, Hao, editor, Doerr, Carola, editor, Emmerich, Michael, editor, and Trautmann, Heike, editor

Published
2020
Full Text
View/download PDF

6. Ensemble Genetic Programming

Author

Rodrigues, Nuno M., Batista, João E., Silva, Sara, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hu, Ting, editor, Lourenço, Nuno, editor, Medvet, Eric, editor, and Divina, Federico, editor

Published
2020
Full Text
View/download PDF

7. A Vectorial Approach to Genetic Programming

Author

Azzali, Irene, Vanneschi, Leonardo, Silva, Sara, Bakurov, Illya, Giacobini, Mario, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Sekanina, Lukas, editor, Hu, Ting, editor, Lourenço, Nuno, editor, Richter, Hendrik, editor, and García-Sánchez, Pablo, editor

Published
2019
Full Text
View/download PDF

9. Local Search is Underused in Genetic Programming

Author

Trujillo, Leonardo, Z-Flores, Emigdio, Juárez-Smith, Perla S., Legrand, Pierrick, Silva, Sara, Castelli, Mauro, Vanneschi, Leonardo, Schütze, Oliver, Muñoz, Luis, Goldberg, David E., Series Editor, Koza, John R., Series Editor, Riolo, Rick, editor, Worzel, Bill, editor, Goldman, Brian, editor, and Tozier, Bill, editor

Published
2018
Full Text
View/download PDF

10. Genetic Programming Representations for Multi-dimensional Feature Learning in Biomedical Classification

Author

La Cava, William, Silva, Sara, Vanneschi, Leonardo, Spector, Lee, Moore, Jason, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Squillero, Giovanni, editor, and Sim, Kevin, editor

Published
2017
Full Text
View/download PDF

11. RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming

Author

López, Uriel, Trujillo, Leonardo, Martinez, Yuliana, Legrand, Pierrick, Naredo, Enrique, Silva, Sara, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, McDermott, James, editor, Castelli, Mauro, editor, Sekanina, Lukas, editor, Haasdijk, Evert, editor, and García-Sánchez, Pablo, editor

Published
2017
Full Text
View/download PDF

15. M3GP – Multiclass Classification with GP

Author

Muñoz, Luis, Silva, Sara, Trujillo, Leonardo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Machado, Penousal, editor, Heywood, Malcolm I., editor, McDermott, James, editor, Castelli, Mauro, editor, García-Sánchez, Pablo, editor, Burelli, Paolo, editor, Risi, Sebastian, editor, and Sim, Kevin, editor

Published
2015
Full Text
View/download PDF

16. Random Sampling Technique for Overfitting Control in Genetic Programming

Author

Gonçalves, Ivo, Silva, Sara, Melo, Joana B., Carreiras, João M. B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Moraglio, Alberto, editor, Silva, Sara, editor, Krawiec, Krzysztof, editor, Machado, Penousal, editor, and Cotta, Carlos, editor

Published
2012
Full Text
View/download PDF

17. An Empirical Study of Functional Complexity as an Indicator of Overfitting in Genetic Programming

Author

Trujillo, Leonardo, Silva, Sara, Legrand, Pierrick, Vanneschi, Leonardo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Silva, Sara, editor, Foster, James A., editor, Nicolau, Miguel, editor, Machado, Penousal, editor, and Giacobini, Mario, editor

Published
2011
Full Text
View/download PDF

18. A Quantitative Study of Learning and Generalization in Genetic Programming

Author

Castelli, Mauro, Manzoni, Luca, Silva, Sara, Vanneschi, Leonardo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Silva, Sara, editor, Foster, James A., editor, Nicolau, Miguel, editor, Machado, Penousal, editor, and Giacobini, Mario, editor

Published
2011
Full Text
View/download PDF

19. A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems

Author

Ingalalli, Vijay, Silva, Sara, Castelli, Mauro, Vanneschi, Leonardo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Nicolau, Miguel, editor, Krawiec, Krzysztof, editor, Heywood, Malcolm I., editor, Castelli, Mauro, editor, García-Sánchez, Pablo, editor, Merelo, Juan J., editor, Rivas Santos, Victor M., editor, and Sim, Kevin, editor

Published
2014
Full Text
View/download PDF

20. ESAGP – A Semantic GP Framework Based on Alignment in the Error Space

Author

Ruberto, Stefano, Vanneschi, Leonardo, Castelli, Mauro, Silva, Sara, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Nicolau, Miguel, editor, Krawiec, Krzysztof, editor, Heywood, Malcolm I., editor, Castelli, Mauro, editor, García-Sánchez, Pablo, editor, Merelo, Juan J., editor, Rivas Santos, Victor M., editor, and Sim, Kevin, editor

Published
2014
Full Text
View/download PDF

21. Balancing Learning and Overfitting in Genetic Programming with Interleaved Sampling of Training Data

Author

Gonçalves, Ivo, Silva, Sara, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Krawiec, Krzysztof, editor, Moraglio, Alberto, editor, Hu, Ting, editor, Etaner-Uyar, A. Şima, editor, and Hu, Bin, editor

Published
2013
Full Text
View/download PDF

22. Prediction of Forest Aboveground Biomass: An Exercise on Avoiding Overfitting

Author

Silva, Sara, Ingalalli, Vijay, Vinga, Susana, Carreiras, João M. B., Melo, Joana B., Castelli, Mauro, Vanneschi, Leonardo, Gonçalves, Ivo, Caldas, José, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Esparcia-Alcázar, Anna I., editor

Published
2013
Full Text
View/download PDF

23. Land Cover/Land Use Multiclass Classification Using GP with Geometric Semantic Operators

Author

Castelli, Mauro, Silva, Sara, Vanneschi, Leonardo, Cabral, Ana, Vasconcelos, Maria J., Catarino, Luís, Carreiras, João M. B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Esparcia-Alcázar, Anna I., editor

Published
2013
Full Text
View/download PDF

25. A Comparison of Machine Learning Methods for the Prediction of Breast Cancer

Author

Silva, Sara, Anunciação, Orlando, Lotz, Marco, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Pizzuti, Clara, editor, Ritchie, Marylyn D., editor, and Giacobini, Mario, editor

Published
2011
Full Text
View/download PDF

27. Application of Genetic Programming Classification in an Industrial Process Resulting in Greenhouse Gas Emission Reductions

Author

Lotz, Marco, Silva, Sara, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Di Chio, Cecilia, editor, Brabazon, Anthony, editor, Di Caro, Gianni A., editor, Ebner, Marc, editor, Farooq, Muddassar, editor, Fink, Andreas, editor, Grahl, Jörn, editor, Greenfield, Gary, editor, Machado, Penousal, editor, O’Neill, Michael, editor, Tarantino, Ernesto, editor, and Urquhart, Neil, editor

Published
2010
Full Text
View/download PDF

28. Bloat Free Genetic Programming versus Classification Trees for Identification of Burned Areas in Satellite Imagery

Author

Silva, Sara, Vasconcelos, Maria J., Melo, Joana B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Di Chio, Cecilia, editor, Cagnoni, Stefano, editor, Cotta, Carlos, editor, Ebner, Marc, editor, Ekárt, Anikó, editor, Esparcia-Alcazar, Anna I., editor, Goh, Chi-Keong, editor, Merelo, Juan J., editor, Neri, Ferrante, editor, Preuß, Mike, editor, Togelius, Julian, editor, and Yannakakis, Georgios N., editor

Published
2010
Full Text
View/download PDF

29. Using Operator Equalisation for Prediction of Drug Toxicity with Genetic Programming

Author

Vanneschi, Leonardo, Silva, Sara, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Lopes, Luís Seabra, editor, Lau, Nuno, editor, Mariano, Pedro, editor, and Rocha, Luís M., editor

Published
2009
Full Text
View/download PDF

30. Extending Operator Equalisation: Fitness Based Self Adaptive Length Distribution for Bloat Free GP

Author

Silva, Sara, Dignum, Stephen, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Vanneschi, Leonardo, editor, Gustafson, Steven, editor, Moraglio, Alberto, editor, De Falco, Ivanoe, editor, and Ebner, Marc, editor

Published
2009
Full Text
View/download PDF

31. Classification of Seafloor Habitats Using Genetic Programming

Author

Silva, Sara, Tseng, Yao-Ting, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Giacobini, Mario, editor, Brabazon, Anthony, editor, Cagnoni, Stefano, editor, Di Caro, Gianni A., editor, Drechsler, Rolf, editor, Ekárt, Anikó, editor, Esparcia-Alcázar, Anna Isabel, editor, Farooq, Muddassar, editor, Fink, Andreas, editor, McCormack, Jon, editor, O’Neill, Michael, editor, Romero, Juan, editor, Rothlauf, Franz, editor, Squillero, Giovanni, editor, Uyar, A. Şima, editor, and Yang, Shengxiang, editor

Published
2008
Full Text
View/download PDF

33. Evolutionary Computation Approaches for Shape Modelling and Fitting

Author

Silva, Sara, Fayolle, Pierre-Alain, Vincent, Johann, Pauron, Guillaume, Rosenberger, Christophe, Toinard, Christian, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Bento, Carlos, editor, Cardoso, Amílcar, editor, and Dias, Gaël, editor

Published
2005
Full Text
View/download PDF

35. Feature Selection Using Genetic Algorithms and Genetic Programming

Author

Rodrigues, Nuno M., Batista, João E., La Cava, William, Vanneschi, Leonardo, Silva, Sara, NOVA Information Management School (NOVA IMS), and Information Management Research Center (MagIC) - NOVA Information Management School

Subjects
Machine Learning, Genetic Algorithms, Epistasis, Genetic Programming, Feature Selection, Theoretical Computer Science, Computer Science(all)
Abstract

Rodrigues, N. M., Batista, J. E., La Cava, W., Vanneschi, L., & Silva, S. (2022). SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. In E. Medvet, G. Pappa, & B. Xue (Eds.), Genetic Programming: 25th European Conference, EuroGP 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings (pp. 68-84). (Lecture Notes in Computer Science; Vol. 13223). Springer. https://doi.org/10.1007/978-3-031-02056-8_5 -------------------------------------------------------------------This work was supported by FCT, Portugal, through funding of LASIGE Research Unit (UIDB/00408/2020 and UIDP/00408/2020); MAR2020 program via project MarCODE (MAR-01.03.01-FEAMP-0047); projects BINDER (PTDC/CCI-INF/29168/2017), AICE (DSAIPA/DS/0113/2019), OPTOX (PTDC/CTA-AMB/30056/2017) and GADgET (DSAIPA/DS/0022/2018). Nuno Rodrigues and João Batista were supported by PhD Grants 2021/05322/BD and SFRH/BD/143972/2019, respectively; William La Cava was supported by the National Library Of Medicine of the National Institutes of Health under Award Number R00LM012926. We present SLUG, a method that uses genetic algorithms as a wrapper for genetic programming (GP), to perform feature selection while inducing models. This method is first tested on four regular binary classification datasets, and then on 10 synthetic datasets produced by GAMETES, a tool for embedding epistatic gene-gene interactions into noisy datasets. We compare the results of SLUG with the ones obtained by other GP-based methods that had already been used on the GAMETES problems, concluding that the proposed approach is very successful, particularly on the epistatic datasets. We discuss the merits and weaknesses of SLUG and its various parts, i.e. the wrapper and the learner, and we perform additional experiments, aimed at comparing SLUG with other state-of-the-art learners, like decision trees, random forests and extreme gradient boosting. Despite the fact that SLUG is not the most efficient method in terms of training time, it is confirmed as the most effective method in terms of accuracy. authorsversion published

Published
2022

40. Applications of Evolutionary Computation

Author

Mora, Antonio M., Squillero, Giovanni, Di Chio, C, Agapitos, Alexandros, Cagnoni, Stefano, Cotta, Carlos, Fernández De Vega, F, Di Caro, G A, Drechsler, R., Ekárt, A, Esparcia-Alcázar, Anna I., Farooq, M, Langdon, W B, Merelo-Guervós, J.J., Preuss, M, Richter, O.-M.H., Silva, Sara, Sim$\$~oes, A, Tarantino, Ernesto, Tettamanzi, Andrea G B, Togelius, J, Urquhart, Neil, Uyar, A S, Yannakakis, G N, Smith, Stephen L, Caserta, Marco, Ramirez, Adriana, Voß, Stefan, Burelli, Paolo, Jan, Mathieu, Matthias, M, Esparcia-Alcazar, Anna I, De Falco, Ivanoe, Cioppa, Antonio Della, Diwold, Konrad, Ekart, Aniko, Vega, Francisco Fernandez De, Sim, Kevin, Simoes, Anabela, Merelo, J.J., Haasdijk, Evert, Zhang, Mengjie, Eiben, A E, Glette, Kyrre, Rohlfshagen, Philipp, Schaefer, Robert, Artificial intelligence, Computational Intelligence, and Network Institute

Subjects
closed-loop control, game theory, energy management system, evolution, pattern recognition, smart building, cartesian genetic programming, agent-based model, algorithm analysis and problem complexity, data mining and knowledge discovery, artificial intelligence (incl. robotics), genetic algorithms, bioinspired methods, ary algorithm, genetic programming, mathematics of computing, battery energy storage system, computer applications, emergent, conformity
Abstract

The application of genetic and evolutionary computation to problems in medicine has increased rapidly over the past five years, but there are specific issues and challenges that distinguish it from other real-world applications. Obtaining reliable and coherent patient data, establishing the clinical need and demonstrating value in the results obtained are all aspects that require careful and detailed consideration. This tutorial is based on research which uses genetic programming (a representation of Cartesian Genetic Programming) in the diagnosis and monitoring of Parkinson's disease, Alzheimer's disease and other neurodegenerative conditions, as well as in the early detection of breast cancer through automated assessment of mammograms. The work is supported by multiple clinical studies in progress in the UK (Leeds General Infirmary), USA (UCSF), UAE (Dubai Rashid Hospital), Australia (Monash Medical Center) and Singapore (National Neuroscience Institute). The technology is protected through three patent applications and a University spin-out company marketing four medical devices. The tutorial considers the following topics: Introduction to medical applications of genetic and evolutionary computation and how these differ from other real-world applications Overview of past work in the from a medical and evolutionary computation point of view Three case examples of medical applications: i. diagnosis and monitoring of Parkinson's disease ii. detection of beast cancer from mammograms iii. cancer screening using Raman spectroscopy Practical advice on how to get started working on medical applications, including existing medical databases and conducting new medical studies, commercialization and protecting intellectual property. Summary, further reading and links

Published
2015

41. Multidimensional genetic programming for multiclass classification.

Author

La Cava, William, Silva, Sara, Danai, Kourosh, Spector, Lee, Vanneschi, Leonardo, and Moore, Jason H.

Subjects
GENETIC programming, COMPUTER programming, GENETIC algorithms, COMBINATORIAL optimization, EVOLUTIONARY algorithms
Abstract

Abstract We describe a new multiclass classification method that learns multidimensional feature transformations using genetic programming. This method optimizes models by first performing a transformation of the feature space into a new space of potentially different dimensionality, and then performing classification using a distance function in the transformed space. We analyze a novel program representation for using genetic programming to represent multidimensional features and compare it to other approaches. Similarly, we analyze the use of a distance metric for classification in comparison to simpler techniques more commonly used when applying genetic programming to multiclass classification. Finally, we compare this method to several state-of-the-art classification techniques across a broad set of problems and show that this technique achieves competitive test accuracies while also producing concise models. We also quantify the scalability of the method on problems of varying dimensionality, sample size, and difficulty. The results suggest the proposed method scales well to large feature spaces. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

42. A semi-supervised Genetic Programming method for dealing with noisy labels and hidden overfitting.

Author

Silva, Sara, Vanneschi, Leonardo, Cabral, Ana I.r., and Vasconcelos, Maria J.

Subjects
GENETIC programming, CART algorithms, HUMAN error, REMOTE-sensing images, SUPERVISED learning
Abstract

Data gathered in the real world normally contains noise, either stemming from inaccurate experimental measurements or introduced by human errors. Our work deals with classification data where the attribute values were accurately measured, but the categories may have been mislabeled by the human in several sample points, resulting in unreliable training data. Genetic Programming (GP) compares favorably with the Classification and Regression Trees (CART) method, but it is still highly affected by these errors. Despite consistently achieving high accuracy in both training and test sets, many classification errors are found in a later validation phase, revealing a previously hidden overfitting to the erroneous data. Furthermore, the evolved models frequently output raw values that are far from the expected range. To improve the behavior of the evolved models, we extend the original training set with additional sample points where the class label is unknown, and devise a simple way for GP to use this additional information and learn in a semi-supervised manner. The results are surprisingly good. In the presence of the exact same mislabeling errors, the additional unlabeled data allowed GP to evolve models that achieved high accuracy also in the validation phase. This is a brand new approach to semi-supervised learning that opens an array of possibilities for making the most of the abundance of unlabeled data available today, in a simple and inexpensive way. [ABSTRACT FROM AUTHOR]

Published
2018
Full Text
View/download PDF

43. The influence of population size in geometric semantic GP.

Author

Castelli, Mauro, Manzoni, Luca, Silva, Sara, Vanneschi, Leonardo, and Popovič, Aleš

Subjects
GEOMETRY, SEMANTIC computing, GENETIC programming, REGRESSION analysis, SEMANTICS
Abstract

In this work, we study the influence of the population size on the learning ability of Geometric Semantic Genetic Programming for the task of symbolic regression. A large set of experiments, considering different population size values on different regression problems, has been performed. Results show that, on real-life problems, having small populations results in a better training fitness with respect to the use of large populations after the same number of fitness evaluations. However, performance on the test instances varies among the different problems: in datasets with a high number of features, models obtained with large populations present a better performance on unseen data, while in datasets characterized by a relative small number of variables a better generalization ability is achieved by using small population size values. When synthetic problems are taken into account, large population size values represent the best option for achieving good quality solutions on both training and test instances. [ABSTRACT FROM AUTHOR]

Published
2017
Full Text
View/download PDF

44. Self-tuning geometric semantic Genetic Programming.

Author

Popovič, Aleš, Castelli, Mauro, Vanneschi, Leonardo, Manzoni, Luca, and Silva, Sara

Abstract

The process of tuning the parameters that characterize evolutionary algorithms is difficult and can be time consuming. This paper presents a self-tuning algorithm for dynamically updating the crossover and mutation probabilities during a run of genetic programming. The genetic operators that are considered in this work are the geometric semantic genetic operators introduced by Moraglio et al. Differently from other existing self-tuning algorithms, the proposed one works by assigning a (different) crossover and mutation probability to each individual of the population. The experimental results we present show the appropriateness of the proposed self-tuning algorithm: on seven different test problems, the proposed algorithm finds solutions of a quality that is better than, or comparable to, the one achieved using the best known values for the geometric semantic crossover and mutation rates for the same problems. Also, we study how the mutation and crossover probabilities change during the execution of the proposed self-tuning algorithm, pointing out an interesting insight: mutation is basically the only operator used in the exploration phase, while crossover is used for exploitation, further improving good quality solutions. [ABSTRACT FROM AUTHOR]

Published
2016
Full Text
View/download PDF

45. Resource-Limited Genetic Programming: Replacing Tree Depth Limits.

Author

Ribeiro, Bernardete, Albrecht, Rudolf F., Dobnikar, Andrej, Pearson, David W., Steele, Nigel C., Silva, Sara, Silva, Pedro J.N., and Costa, Ernesto

Subjects
GENETIC programming, GENETIC algorithms, COMPUTER programming, COMBINATORIAL optimization, ALGORITHMS
Abstract

We propose replacing the traditional tree depth limit in Genetic Programming by a single limit on the amount of resources available to the whole population, where resources are the tree nodes. The resource-limited technique removes the disadvantages of using depth limits at the individual level, while introducing automatic population resizing, a natural side-effect of using an approach at the population level. The results show that the replacement of individual depth limits by a population resource limit can be done without impairing performance, thus validating this first and important step towards a new approach to improving the efficiency of GP. [ABSTRACT FROM AUTHOR]

Published
2005
Full Text
View/download PDF

46. Improving Land Cover Classification Using Genetic Programming for Feature Construction.

Author

Batista, João E., Cabral, Ana I. R., Vasconcelos, Maria J. P., Vanneschi, Leonardo, Silva, Sara, and Pasolli, Edoardo

Subjects
LAND cover, ZONING, GENETIC programming, PROBLEM solving, RANDOM forest algorithms, REMOTE-sensing images, ENGINEERING standards
Abstract

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

47. Towards the use of vector based GP to predict physiological time series.

Author

Azzali, Irene, Vanneschi, Leonardo, Bakurov, Illya, Silva, Sara, Ivaldi, Marco, and Giacobini, Mario

Subjects
GENETIC programming, TIME series analysis, RECURRENT neural networks, VENTILATION, HEART beat, FORECASTING, VECTOR autoregression model
Abstract

Prediction of physiological time series is frequently approached by means of machine learning (ML) algorithms. However, most ML techniques are not able to directly manage time series, thus they do not exploit all the useful information such as patterns, peaks and regularities provided by the time dimension. Besides advanced ML methods such as recurrent neural network that preserve the ordered nature of time series, a recently developed approach of genetic programming, VE-GP, looks promising on the problem in analysis. VE-GP allows time series as terminals in the form of a vector, including new strategies to exploit this representation. In this paper we compare different ML techniques on the real problem of predicting ventilation flow from physiological variables with the aim of highlighting the potential of VE-GP. Experimental results show the advantage of applying this technique in the problem and we ascribe the good performances to the ability of properly catching meaningful information from time series. • Ventilation flow is important for quantifying the inhaled load of pollutants. • Machine learning is suitable for complex and heterogeneous data of healthcare domain. • Peaks and regularities of heart rate series are informative about ventilation flows. • Vector based GP may become a new support tool to predict flowing physiological parameters. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

48. Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes.

Author

Marques da Silva, Jorge, Figueiredo, Andreia, Cunha, Jorge, Eiras-Dias, José Eduardo, Silva, Sara, Vanneschi, Leonardo, and Mariano, Pedro

Subjects
CHLOROPHYLL spectra, GRAPES, GENOTYPE-environment interaction, GENETIC programming, ARTIFICIAL neural networks, CLIMBING plants
Abstract

When a dark-adapted leaf is illuminated with saturating light, a fast polyphasic rise of fluorescence emission (Kautsky effect) is observed. The shape of the curve is dependent on the molecular organization of the photochemical apparatus, which in turn is a function of the interaction between genotype and environment. In this paper, we evaluate the potential of rapid fluorescence transients, aided by machine learning techniques, to classify plant genotypes. We present results of the application of several machine learning algorithms (k-nearest neighbors, decision trees, artificial neural networks, genetic programming) to rapid induction curves recorded in different species and cultivars of vine grown in the same environmental conditions. The phylogenetic relations between the selected Vitis species and Vitis vinifera cultivars were established with molecular markers. Both neural networks (71.8%) and genetic programming (75.3%) presented much higher global classification success rates than k-nearest neighbors (58.5%) or decision trees (51.6%), genetic programming performing slightly better than neural networks. However, compared with a random classifier (success rate = 14%), even the less successful algorithms were good at the task of classifying. The use of rapid fluorescence transients, handled by genetic programming, for rapid preliminary classification of Vitis genotypes is foreseen as feasible. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

49. GP-Based Generative Adversarial Models

Author

Machado, Penousal, Baeta, Francisco, Martins, Tiago, Correia, João, Banzhaf, Wolfgang, Series Editor, Deb, Kalyanmoy, Series Editor, Trujillo, Leonardo, editor, Winkler, Stephan M., editor, and Silva, Sara, editor

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results