School of Computing

Publications by Dr Fernando Otero

Also view these in the Kent Academic Repository

Article
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] 10:127-135. Available at: http://dx.doi.org/10.1504/IJBIC.2017.085894.
Kampouridis, M. and Otero, F. (2017). 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.
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.
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.
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.
Albinati, J., Oliveira, S., Otero, F. and Pappa, G. (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.
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.
Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery. (2013). 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. (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.
Otero, F. (2017). MYRA: A Java Ant Colony Optimization Framework for Classification Algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York, USA: ACM, pp. 1247-1254. Available at: https://dx.doi.org/10.1145/3067695.3082471.
Helal, A. and Otero, F. (2017). Automatic design of ant-miner mixed attributes for classification rule discovery. In: Proceedings of the Genetic and Evolutionary Computation Conference. New York, USA: ACM, pp. 433-440. Available at: http://dx.doi.org/10.1145/3071178.3071306.
Oliveira, L., Otero, F., Pappa, G. and Albinati, J. (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. and Kampouridis, M. (2014). A Comparative Study on the Use of Classification Algorithms in Financial Forecasting. In: Applications of Evolutionary Computation 17th European Conference. Berlin, Germany: Springer-Verlag, pp. 276-287. Available at: http://dx.doi.org/10.1007/978-3-662-45523-4_23.
Menendez, H., Otero, F. and Camacho, D. (2014). MACOC: a medoid-based ACO clustering algorithm. In: Swarm Intelligence 9th International Conference. Cham, Switzerland: Springer, pp. 122-133. Available at: http://dx.doi.org/10.1007/978-3-319-09952-1_11.
Menendez, H., Otero, F. and Camacho, D. (2014). SACOC: A spectral-based ACO clustering algorithm. In: Intelligent Distributed Computing VIII. Cham, Switzerland: Springer, pp. 185-194. Available at: http://dx.doi.org/10.1007/978-3-319-10422-5_20.
Brookhouse, J., Otero, F. and Kampouridis, M. (2014). Working with OpenCL to Speed Up a Genetic Programming Financial Forecasting Algorithm: Initial Results. In: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation. New York, USA: ACM, pp. 1117-1124. Available at: http://dx.doi.org/10.1145/2598394.2605689.
Salama, K. and Otero, F. (2012). Exploring Different Functions for Heuristics, Discretization, and Rule Quality Evaluation in Ant-Miner. In: Swarm Intelligence 8th International Conference. Berlin, Germany: Springer, pp. 344-345. Available at: https://doi.org/10.1007/978-3-642-32650-9_38.
Otero, F., Segond, M., Freitas, A., Johnson, C., Robilliard, D. and Fonlupt, C. (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.
Otero, F., Freitas, A. and Johnson, C. (2009). A hierarchical classification ant colony algorithm for predicting gene ontology terms. In: Pizzuti, C., Ritchie, M. and Giacobini, M. eds. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics 7th European Conference. Berlin, Germany: 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 6th International Conference. Berlin, Germany: Springer, pp. 48-59. Available at: http://dx.doi.org/10.1007/978-3-540-87527-7_5.
Otero, F., Silva, M., Freitas, A. and Nievola, J. (2003). Genetic Programming for Attribute Construction in Data Mining. In: Ryan, C., Keijzer, M., Poli, R., Soule, T., Tsang, E. and Costa, E. eds. Genetic Programming 6th European Conference. Berlin, Germany: Springer, 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: Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation. San Francisco, California, USA: Morgan Kaufmann, pp. 1270-1270.
Conference or workshop item
Ovchinnik, S., Otero, F. and Freitas, A. (2019). Monotonicity Detection and Enforcement in Longitudinal Classification. In: Bramer, M. and Petridis, M. eds. 39th SGAI International Conference on Artificial Intelligence, AI 2019. Springer. Available at: http://dx.doi.org/10.1007/978-3-030-34885-4_5.
Mohamed, I. and Otero, F. (2019). Using Population-based Metaheuristics and Trend Representative Testing to Compose Strategies for Market Timing. In: 11th International Joint Conference on Computational Intelligence (IJCCI 2019). SciTePress, pp. 59-69. Available at: http://dx.doi.org/10.5220/0008066100590069.
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. USA: 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.
Oliveira, L., Miranda, L., Pappa, G., Otero, F. and Takahashi, R. (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). Monotonicity in Ant Colony Classification Algorithms. In: 10th International Conference on Swarm Intelligence (ANTS 2016). Cham, Switzerland: Springer, pp. 137-148. Available at: http://dx.doi.org/10.1007/978-3-319-44427-7_12.
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.
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.
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.
Oliveira, L., Otero, F., Miranda, L. and Pappa, G. (2016). Revisiting the Sequential Symbolic Regression Genetic Programming. In: Brazilian Conference on Intelligent System (BRACIS 2016). IEEE, pp. 163-168. Available at: http://dx.doi.org/10.1109/BRACIS.2016.039.
Santos, V., Otero, F., Johnson, C., Osorio, F. and Toledo, C. (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.
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.
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.
Albinati, J., Pappa, G., Otero, F. and Oliveira, L. (2015). The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems. In: 18th European Conference on Genetic Programming (EuroGP 2015). Cham, Switzerland: Springer, 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. and Squillero, G. eds. 18th European Conference on Applications of Evolutionary Computation, EvoStar 2015. Springer, pp. 1-12.
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). New York, USA: ACM, pp. 1149-1154. Available at: http://dx.doi.org/10.1145/2598394.2605691.
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). IEEE, pp. 55-60. Available at: http://dx.doi.org/10.1109/TAAI.2013.24.
Vaseux, L., Otero, F., Castle, T. and Johnson, C. (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.
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). IEEE, pp. 900-907. Available at: http://dx.doi.org/10.1109/CEC.2013.6557663.
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.
Medland, M., Otero, F. and Freitas, A. (2012). Improving the cAnt-MinerPB Classification Algorithm. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A. L., Engelbrecht, A. P., Groß, R. and Stützle, T. eds. 8th International Conference on Swarm Intelligence (ANTS 2012). Springer Berlin Heidelberg, pp. 73-84. Available at: http://dx.doi.org/10.1007/978-3-642-32650-9.
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.
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., Otero, F., Johnson, C., Thompson, S. and Freitas, A. (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.
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., Johnson, C., Freitas, A. and Thompson, S. (2010). Refactoring in Automatically Generated Programs. In: 2nd International Symposium on Search Based Software Engineering.
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, pp. 225-231. Available at: http://dx/doi.org/10.1109/CIDM.2009.4938653.
Otero, F. and Freitas, A. (2000). Sumarizacao de textos usando algoritmos de classificacao. In: Lethelier, E., Bortolozzi, F., Weber, K. and Pereira, H. 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
Otero, F. (2013). Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). [Online]. Vol. 512. Terrazas, G., Otero, F. E. and Masegosa, A. D. eds. Springer. Available at: http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-319-01691-7.
Forthcoming
Santos, V., Otero, F., Johnson, C., Osorio, F. and Toledo, C. (2020). Exploratory Path Planning for Mobile Robots in Dynamic Environments with Ant Colony Optimization. In: Genetic and Evolutionary Computation Conference (GECCO '20). Available at: https://doi.org/10.1145/3377930.3390219.
Mohamed, I. and Otero, F. (2020). A Multiobjective Optimization Approach for Market Timing. In: Genetic and Evolutionary Computation Conference (GECCO '20). Available at: https://doi.org/10.1145/3377930.3390156.
Total publications in KAR: 59 [See all in KAR]

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Last Updated: 29/05/2020