School of Computing

Publications by Dr Fernando Otero

Also view these in the Kent Academic Repository

Article
Kampouridis, M. and Otero, F. (2017). Heuristic procedures for improving the predictability of a genetic programming financial forecasting algorithm. Soft Computing [Online] 21:295-310. Available at: http://dx.doi.org/10.1007/s00500-015-1614-8.
Menendez, H., Otero, F. and Camacho, D. (2017). Extending the SACOC algorithm through the Nystrom method for Dense Manifold Data Analysis. International Journal of Bio-Inspired Computation [Online] 10. Available at: http://dx.doi.org/10.1504/IJBIC.2017.085894.
Menendez, H., Otero, F. and Camacho, D. (2016). Medoid-based clustering using ant colony optimization. Swarm Intelligence [Online] 10:123-145. Available at: http://dx.doi.org/10.1007/s11721-016-0122-5.
Kampouridis, M., Otero, F. and Kampouridis, M. (2016). Evolving Trading Strategies Using Directional Changes. Expert Systems with Applications [Online] 73:145-160. Available at: http://dx.doi.org/10.1016/j.eswa.2016.12.032.
Otero, F. and Freitas, A. (2016). Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results. Evolutionary Computation [Online] 24:385-409. Available at: http://dx.doi.org/10.1162/EVCO_a_00155.
Albinati, J. et al. (2015). An ant colony-based semi-supervised approach for learning classification rules. Swarm Intelligence [Online] 9:315-341. Available at: http://dx.doi.org/10.1007/s11721-015-0116-8.
Otero, F., Masegosa, A. and Terrazas, G. (2014). Thematic issue on advances in nature inspired cooperative strategies for optimization. Memetic Computing [Online] 6:147-148. Available at: http://dx.doi.org/10.1007/s12293-014-0140-z.
Salama, K. et al. (2013). Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery. Applied Soft Computing [Online] 13:667-675. Available at: http://dx.doi.org/10.1016/j.asoc.2012.07.026.
Otero, F., Freitas, A. and Johnson, C. (2013). A new sequential covering strategy for inducing classification rules with ant colony algorithms. IEEE Transactions on Evolutionary Computation [Online] 17:64-76. Available at: http://dx.doi.org/10.1109/TEVC.2012.2185846.
Otero, F., Freitas, A. and Johnson, C. (2012). Inducing decision trees with an ant colony optimization algorithm. Applied Soft Computing [Online] 12:3615-3626. Available at: http://dx.doi.org/10.1016/j.asoc.2012.05.028.
Otero, F., Freitas, A. and Johnson, C. (2010). A hierarchical multi-label classification ant colony algorithm for protein function prediction. Memetic Computing [Online] 2:165-181. Available at: http://dx.doi.org/10.1007/s12293-010-0045-4.
Book section
Oliveira, L., Otero, F. and Pappa, G. (2018). A Generic Framework for Building Dispersion Operators in the Semantic Space. in: Genetic Programming Theory and Practice XIV. Springer, pp. 179-195. Available at: http://dx.doi.org/10.1007/978-3-319-97088-2_12.
Oliveira, L. et al. (2015). Sequential Symbolic Regression with Genetic Programming. in: Worzel, B., Kotanchek, M. and Riolo, R. eds. Genetic Programming Theory and Practice XII. Springer, pp. 73-90. Available at: http://dx.doi.org/10.1007/978-3-319-16030-6_5.
Otero, F. et al. (2009). An empirical evaluation of the effectiveness of different types of predictor attributes in protein function prediction. in: Abraham, A., Hassanien, A. -E. and Snasel, V. eds. Studies in Computational Intelligence. Berlin: Springer, pp. 339-357. Available at: http://dx.doi.org/10.1007/978-3-642-01536-6_13.
Conference or workshop item
Brookhouse, J. and Otero, F. (2018). Post-Processing Methods to Enforce Monotonic Constraints in Ant Colony Classification Algorithms. in: 2018 International Joint Conference on Neural Networks. IEEE, pp. 1-8. Available at: http://dx.doi.org/10.1109/IJCNN.2018.8489543.
Helal, A., Brookhouse, J. and Otero, F. (2018). Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization. in: 2018 IEEE Congress on Evolutionary Computation. IEEE, pp. 1-7. Available at: http://dx.doi.org/10.1109/CEC.2018.8477643.
Otero, F. (2017). MYRA: A Java Ant Colony Optimization Framework for Classification Algorithms. in: Genetic and Evolutionary Computation Conference (GECCO 2017). New York, USA: ACM, pp. 1247-1254. Available at: https://doi.org/10.1145/3067695.3082471.
Helal, A. and Otero, F. (2017). Automatic design of ant-miner mixed attributes for classification rule discovery. in: Genetic and Evolutionary Computation (GECCO 2017). ACM, pp. 433-440. Available at: http://dx.doi.org/10.1145/3071178.3071306.
Oliveira, L. et al. (2016). Revisiting the Sequential Symbolic Regression Genetic Programming. in: Brazilian Conference on Intelligent System (BRACIS 2016). pp. 163-168. Available at: http://dx.doi.org/10.1109/BRACIS.2016.039.
Helal, A. and Otero, F. (2016). A Mixed-Attribute Approach in Ant-Miner Classification Rule Discovery Algorithm. in: Genetic and Evolutionary Computation Conference (GECCO 2016). ACM Press, pp. 13-20. Available at: http://dx.doi.org/10.1145/2908812.2908900.
Brookhouse, J. and Otero, F. (2016). Monotonicity in Ant Colony Classification Algorithms. in: 10th International Conference on Swarm Intelligence (ANTS 2016). Springer-Verlag Berlin, pp. 137-148. Available at: http://dx.doi.org/10.1007/978-3-319-44427-7_12.
Oliveira, L., Otero, F. and Pappa, G. (2016). A Dispersion Operator for Geometric Semantic Genetic Programming. in: Genetic and Evolutionary Computation Conference (GECCO 2016). ACM Press, pp. 773-780. Available at: http://dx.doi.org/10.1145/2908812.2908923.
Santos, V. et al. (2016). Exploratory Path Planning Using the Max-Min Ant System Algorithm. in: 2016 IEEE Congress on Evolutionary Computation. pp. 4229-4235. Available at: http://dx.doi.org/10.1109/CEC.2016.7744327.
Oliveira, L. et al. (2016). Reducing Dimensionality to Improve Search in Semantic Genetic Programming. in: 14th International Conference on Parallel Problem Solving from Nature (PPSN 2016). Springer, pp. 375-385. Available at: http://dx.doi.org/10.1007/978-3-319-45823-6_35.
Brookhouse, J. and Otero, F. (2016). Using an Ant Colony Optimization Algorithm for Monotonic Regression Rule Discovery. in: Genetic and Evolutionary Computation Conference (GECCO 2016). ACM Press, pp. 437-444. Available at: http://dx.doi.org/10.1145/2908812.2908896.
Albinati, J., Pappa, G. and Otero, F. (2015). The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems. in: 18th European Conference on Genetic Programming (EuroGP 2015). pp. 3-15. Available at: http://dx.doi.org/10.1007/978-3-319-16501-1_1.
Gypteau, J., Otero, F. and Kampouridis, M. (2015). Generating Directional Change Based Trading Strategies with Genetic Programming. in: Mora, A. M. and Squillero, G. eds. EvoApplications, EvoStar 2015. Springer, pp. 1-12.
Brookhouse, J. and Otero, F. (2015). Discovering Regression Rules with Ant Colony Optimization. in: Genetic and Evolutionary Computation Conference Companion (GECCO'15 Companion). ACM Press, pp. 1005-1012. Available at: http://dx.doi.org/10.1145/2739482.2768450.
Salama, K., Abdelbar, A. and Otero, F. (2015). Investigating Evaluation Measures in Ant Colony Algorithms for Learning Decision Tree Classifiers. in: 2015 IEEE Symposium Series on Computational Intelligence. IEEE, pp. 1146-1153. Available at: http://dx.doi.org/10.1109/SSCI.2015.164.
Menendez, H., Otero, F. and Camacho, D. (2014). SACOC: A spectral-based ACO clustering algorithm. in: 8th International Symposium on Intelligent Distributed Computing (IDC 2014). pp. 185-194. Available at: http://dx.doi.org/10.1007/978-3-319-10422-5_20.
Otero, F. and Kampouridis, M. (2014). A Comparative Study on the Use of Classification Algorithms in Financial Forecasting. in: EvoApplications 2014. Springer-Verlag Berlin, pp. 276-287. Available at: http://dx.doi.org/10.1007/978-3-662-45523-4_23.
Salama, K. and Otero, F. (2014). Learning Multi-Tree Classification Models with Ant Colony Optimization. in: 6th International Conference on Evolutionary Computation Theory and Applications (ECTA 2014). INSTICC Press, pp. 38-48. Available at: http://dx.doi.org/10.5220/0005071300380048.
Leroux, C., Otero, F. and Johnson, C. (2014). A Genetic Programming Problem Definition Language Code Generator for the EpochX Framework. in: 16th International Conference on Genetic and Evolutionary Computation (GECCO 2014). pp. 1149-1154. Available at: http://dx.doi.org/10.1145/2598394.2605691.
Menendez, H., Otero, F. and Camacho, D. (2014). MACOC: a medoid-based ACO clustering algorithm. in: 9th International Conference on Swarm Intelligence (ANTS 2014). pp. 122-133. Available at: http://dx.doi.org/10.1007/978-3-319-09952-1_11.
Brookhouse, J., Otero, F. and Kampouridis, M. (2014). Working with OpenCL to Speed Up a Genetic Programming Financial Forecasting Algorithm: Initial Results. in: 16th International Conference on Genetic and Evolutionary Computation (GECCO 2014). pp. 1117-1124. Available at: http://dx.doi.org/10.1145/2598394.2605689.
Salama, K. and Otero, F. (2013). Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner. in: IEEE Congress on Evolutionary Computation (IEEE CEC 2013). pp. 900-907. Available at: http://dx.doi.org/10.1109/CEC.2013.6557663.
Kampouridis, M. and Otero, F. (2013). Using attribute construction to improve the predictability of a GP financial forecasting algorithm. in: Technologies and Applications of Artificial Intelligence (TAAI 2013). pp. 55-60. Available at: http://dx.doi.org/10.1109/TAAI.2013.24.
Otero, F. and Johnson, C. (2013). Automated Problem Decomposition for the Boolean Domain with Genetic Programming. in: 16th European Conference on Genetic Programming (EuroGP 2013). Springer, pp. 169-180. Available at: http://dx.doi.org/10.1007/978-3-642-37207-0_15.
Vaseux, L. et al. (2013). Event-based graphical monitoring in the EpochX genetic programming framework. in: 15th International Conference on Genetic and Evolutionary Computation (GECCO 2013). pp. 1309-1316. Available at: http://dx.doi.org/10.1145/2464576.2482710.
Otero, F. and Freitas, A. (2013). Improving the interpretability of classification rules discovered by an ant colony algorithm. in: 2013 Genetic and Evolutionary Computation Conference (GECCO'13). New York, NY, USA.: ACM Press., pp. 73-80.
Otero, F., Castle, T. and Johnson, C. (2012). EpochX: Genetic Programming in Java with Statistics and Event Monitoring. in: Proceedings of the 2012 Genetic and Evolutionary Conference Companion (GECCO 2012). Philadelphia: ACM Press. Available at: http://dx.doi.org/10.1145/2330784.2330800.
Moraglio, A. et al. (2012). Evolving Recursive Programs using Non-recursive Scaffolding. in: Proceedings of the 2012 IEEE World Congress on Computational Intelligence. pp. 1596-1603. Available at: http://www.cs.kent.ac.uk/pubs/2012/3225.
Medland, M. and Otero, F. (2012). A Study of Different Quality Evaluation Functions in the cAnt-MinerPB Classification Algorithm. in: Proceedings of the 2012 Genetic and Evolutionary Conference (GECCO 2012). ACM Press, pp. 49-55. Available at: http://www.cs.kent.ac.uk/pubs/2012/3244.
Medland, M., Otero, F. and Freitas, A. (2012). Improving the cAnt-MinerPB Classification Algorithm. in: Dorigo, M. et al. eds. Swarm Intelligence. Springer Berlin Heidelberg, pp. 73-84. Available at: http://dx.doi.org/10.1007/978-3-642-32650-9.
Moraglio, A., Otero, F. and Johnson, C. (2010). The ACO Encoding. in: Dorigo, M. ed. Swarm Intelligence - 7th International Conference (ANTS 2010). pp. 182-196. Available at: http://www.cs.kent.ac.uk/pubs/2010/3176.
Otero, F., Freitas, A. and Johnson, C. (2009). Handling continuous attributes in ant colony classification algorithms. in: Proc. of the 2009 IEEE Symposium on Computational Intelligence in Data Mining (CIDM 2009). IEEE Press, pp. 225-231. Available at: http://dx/doi.org/10.1109/CIDM.2009.4938653.
Otero, F., Freitas, A. and Johnson, C. (2009). A hierarchical classification ant colony algorithm for predicting gene ontology terms. in: Pizzuti, C., Ritchie, M. D. and Giacobini, M. eds. Proc. 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2009). Springer, pp. 68-79. Available at: http://dx.doi.org/10.1007/978-3-642-01184-9_7.
Otero, F., Freitas, A. and Johnson, C. (2008). cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes. in: Dorigo, M. ed. Ant Colony Optimization and Swarm Intelligence (Proc. ANTS 2008), LNCS 5217. Springer, pp. 48-59. Available at: http://dx.doi.org/10.1007/978-3-540-87527-7_5.
Otero, F. et al. (2003). Genetic Programming for Attribute Construction in Data Mining. in: Ryan, C. et al. eds. Genetic Programming: Proc. 6th European Conference (EuroGP-2003). Springer-Verlag, pp. 384-393. Available at: http://dx.doi.org/10.1007/3-540-36599-0_36.
Otero, F., Silva, M. and Freitas, A. (2002). Genetic Programming for Attribute Construction in Data Mining. in: Proc Genetic and Evolutionary Computation Conf (GECCO-2002). San Francisco: Morgan Kaufmann, Publishers, pp. 1270-1270.
Otero, F. and Freitas, A. (2000). Sumarizacao de textos usando algoritmos de classificacao. in: Lethelier, E. et al. eds. Proceedings of the 2000 International Symposium on Knowledge Management/Document Management (ISKM/DM-2000). Curitiba, Brazil: Editora Universitaria Champagnat (PUCPR), pp. 347-357.
Thesis
Otero, F. (2010). New ant colony optimisation algorithms for hierarchial classification of protein functions. University of Kent. Available at: http://www.cs.kent.ac.uk/pubs/2010/3057.
Edited book
Terrazas, G., Otero, F.E.B. and Masegosa, A.D. eds. (2013). Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). [Online]. Springer. Available at: http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-319-01691-7.
Total publications in KAR: 53 [See all in KAR]

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Last Updated: 20/11/2018