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School of Computing

Publications by Dr Alex Freitas

Publication period   (9/1992 onwards)   -   ordered by publication type and date

Books

• Automating the design of data mining algorithms: an evolutionary computation approach. G.L. Pappa and A.A. Freitas. Natural Computing Series. Springer, January 2010.
• Data Mining and Knowledge Discovery with Evolutionary Algorithms. AA Freitas. Spinger-Verlag, Berlin, 2002.
• Mining Very Large Databases with Parallel Processing. AA Freitas and SH Lavington. Kluwer Academic Publishers, Boston, 1998.

Journal Papers

• Multiple pheromone types and other extensions to the ant-miner classification rule discovery algorithm.. K.M. Salama, A.M. Abdelbar, and A.A. Freitas. Swarm Intelligence, 5(3-4):149-182, December 2011.
• Adapting non-hierarchical multilabel classification methods for hierarchical multilabel classification. R. Cerri, A.C.P.L.F. de Carvalho, and A.A. Freitas. Intelligent Data Analysis, 15(6):861-887, November 2011.
• Selecting different protein representations and classification algorithms in hierarchical protein function prediction. C.N. Silla Jr. and A.A. Freitas. Intelligent Data Analysis, 15(6):979-999, November 2011.
• Improving lazy attribute selection. R.B. Pereira, A. Plastino, B. Zadrozny, L.H.C. Merschmann, and A.A. Freitas. Journal of Information and Data Management, 2(3):447-462, October 2011.
• Lazy attribute selection: Choosing attributes at classification time.. R.B. Pereira, A. Plastino, B. Zadrozny, L.H.C. Merschmann, and A.A. Freitas. Intelligent Data Analysis, 15(5):715-732, September 2011.
• A genetic programming method for protein motif discovery and protein classification. D.F. Tsunoda, A.A. Freitas, and H.S. Lopes. Soft Computing, 15(10):1897-1908, September 2011.
• Present perspectives on the automated classification of the g-protein coupled receptors (gpcrs) at the protein sequence level. M.N. Davies, D.E. Gloriam, A. Secker, A.A. Freitas, J. Timmis, and D.R.Flower. Current Topics in Medicinal Chemistry, 11(15):1994-2009, August 2011.
• A review and appraisal of the dna damage theory of ageing. A.A. Freitas and J.P. de Magalhaes. Mutation Research, 728(1-2):12-22, July 2011.
• A hybrid data mining metaheuristic for the p-median problem. A. Plastino, E.R. Fonseca, R. Fuchshuber, S.L. Martins, A.A. Freitas, M. Luis, and S. Salhi. Statistical Analysis and Data Mining, 4(3):313-335, June 2011.
• A data mining approach for classifying dna repair genes into ageing-related or non-ageing-related. A.A. Freitas, O. Vasieva, and J.P. de Magalhaes. BMC Genomics, 12(27):11 pages, January 2011.
• A survey of hierarchical classification across different application domains. C.N. Silla Jr. and A.A. Freitas. Data Mining and Knowledge Discovery, 22(1-2):31-72, January 2011.
• A hierarchical multi-label classification ant colony algorithm for protein function prediction. F.E.B. Otero, A.A. Freitas, and C.G. Johnson. Memetic Computing, 2(3):165-181, September 2010.
• Learning hybridization strategies in evolutionary algorithms. A. LaTorre, J.M. Pena, S. Muelas, and A.A. Freitas. Intelligent Data Analysis, 14(3):333-354, July 2010.
• On the importance of comprehensible classification models for protein function prediction. A.A. Freitas, D.C. Wieser, and R. Apweiler. IEEE/ACM Trans. on Computational Biology and Bioinformatics, 7(1):172-182, January 2010.
• A hierarchical multi-label classification ant colony algorithm for protein function prediction. F.E.B. Otero, A.A. Freitas, and C.G. Johnson. Memetic Computing, 2(3):165-181, January 2010.
• Hierarchical classification of g-protein-coupled receptors with data-driven selection of attributes and classifiers. A. Secker, M.N. Davies, A.A. Freitas, E. Clark, J. Timmis, and D.R. Flower. International Journal of Data Mining and Bioinformatics, 4(2):191-210, January 2010.
• Evolving rule induction algorithms with multi-objective grammar-based genetic programming. G.L. Pappa and A.A. Freitas. Knowledge and Information Systems, 19(3):283-309, June 2009.
• An artificial immune system for clustering amino acids in the context of protein function classification. A. Secker, M.N. Davies, A.A. Freitas, J. Timmis, E. Clark, and D.R. Flower. Journal of Mathematical Modelling and Algorithms, 8:103-123, June 2009.
• Automatically evolving rule induction algorithms tailored to the prediction of postsynaptic activity in proteins. G.L. Pappa and A.A. Freitas. Intelligent Data Analysis, 13(2):243-259, May 2009.
• A survey of evolutionary algorithms for clustering. E.R. Hruschka, R.J.G.B. Campello, A.A. Freitas, and A.C.P.L.F. de Carvalho. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 39(2):133-155, March 2009.
• A survey of evolutionary algorithms for clustering. E.R. Hruschka, R.J.G.B. Campello, A.A. Freitas, and A.C.P.L.F. de Carvalho. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 39(2):133-155, March 2009.
• Hierarchical classification of protein function with ensembles of rules and particle swarm optimisation. N. Holden and A.A. Freitas. Soft Computing, 13(3):259-272, February 2009.
• Lexicographic multi-objective evolutionary induction of decision trees. M.P. Basgalupp, A.C.P.L.F. de Carvalho, R.C. Barros, D.D. Ruiz, and A.A. Freitas. International Journal of Bio-Inspired Computation, 1(1/2):105-117, January 2009.
• Alignment-independent techniques for protein classification. M.N. Davies, A. Secker, A.A. Freitas, J. Timmis, E. Clark, and D.R. Flower. Current Proteomics, 5(4):217-223, December 2008.
• Robust autonomous detection of the defective pixels in detectors using a probabilistic technique. Siddhartha Ghosh, Dirk Froebrich, and Alex Freitas. Applied Optics, 47(36):6904-6924, December 2008.
• Optimizing amino acid groupings for GPCR classification. M.N. Davies, A. Secker, A.A. Freitas, E. Clark, J. Timmis, and D.R. Flower. Bioinformatics, 24(18):1980-1986, September 2008.
• Message-passing algorithms for the prediction of protein domain interactions from protein–protein interaction data. Mudassar Iqbal, Alex A. Freitas, Colin G. Johnson, and Massimo Vergassola. Bioinformatics, 24(18):2064-2070, September 2008.
• GPCRTree: online hierarchical classification of GPCR function. M.N. Davies, A. Secker, M. Halling-Brown, D.S. Moss, A.A. Freitas, J. Timmis, E. Clark, and D.R. Flower. BMC Research Notes, 1(67):5 pages, August 2008.
• A hybrid pso/aco algorithm for discovering classification rules in data mining. N. Holden and A.A. Freitas. Journal of Artificial Evolution and Applications, 2008:11 pages, May 2008.
• Aisiid: an artificial immune system for interesting information discovery on the web. Andrew Secker, Alex A. Freitas, and Jon Timmis. Applied Soft Computing, 8(2):885-905, March 2008.
• On the hierarchical classification of g protein-coupled receptors. Matthew N. Davies, Andrew Secker, Alex A. Freitas, Miguel Mendao, Jon Timmis, and Darren R. Flower. Bioinformatics, 23(23):3113-3118, December 2007.
• An experimental comparison of classification algorithms for hierarchical prediction of protein function. Andrew Secker, Matthew N. Davies, Alex A. Freitas, Jon Timmis, Miguel Mendao, and Darren R. Flower. Expert Update (Magazine of the British Computer Society's Specialist Group on AI), 9(3):17-22, November 2007.
• Proteomic applications of automated gpcr classification. M.N. Davies, D.E. Gloriam, A. Secker, A.A. Freitas, M. Mendao, J. Timmis, and D.R. Flower. Proteomics, 7(16):2800-2814, August 2007.
• Revisiting the foundations of artificial immune systems for data mining. A.A. Freitas and J. Timmis. IEEE Transactions on Evolutionary Computation, 11(4):521-540, August 2007.
• Are we really discovering "interesting" knowledge from data?. A.A. Freitas. Expert Update (the BCS-SGAI Magazine), 9(1):41-47, October 2006.
• Evaluating six candidate solutions for the small-disjunct problem and choosing the best solution via meta-learning. DR Carvalho and AA Freitas. Artificial Intelligence Review, 24(1):61-98, September 2005.
• Predicting post-synaptic activity in proteins with data mining. GL Pappa, AJ Baines, and AA Freitas. Bioinformatics, 21(Suppl. 2):ii19-ii25, September 2005.
• A critical review of multi-objective optimization in data mining: a position paper. Alex Freitas. SIGKDD Explorations, 6(2):77-86, December 2004.
• A hybrid decision tree/genetic algorithm method for data mining. DR Carvalho and AA Freitas. Information Sciences, 163(1-3):13-35, June 2004.
• Discovering interesting knowledge from a science & technology database with a genetic algorithm. W Romao, AA Freitas, and IMS Gimenes. Applied Soft Computing, 4(2):121-137, May 2004.
• A genetic algorithm for solving a capacitated p-median problem. ES Correa, MTA Steiner, AA Freitas, and C Carnieri. Numerical Algorithms, 35(2-4):373-388, April 2004.
• A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets. CC Bojarczuk, HS Lopes, AA Freitas, and EL Michalkiewicz. Artificial Intelligence in Medicine, 30:27-48, January 2004.
• Guest editorial: Data mining and knowledge discovery with evolutionary algorithms. A Ghosh and AA Freitas. IEEE Trans. on Evolutionary Computation, 7(6):517-518, December 2003.
• A genetic algorithm for discovering small disjunct rules in data mining. DR Carvalho and AA Freitas. Applied Soft Computing, 2(2):75-88, December 2002.
• Data Mining with an Ant Colony Optimization Algorithm. RS Parpinelli, HS Lopes, and AA Freitas. IEEE Trans on Evolutionary Computation, special issue on Ant Colony Algorithms, 6(4):321-332, August 2002.
• Understanding the crucial role of attribute interaction in data mining. AA Freitas. Artificial Intelligence Review, 16(3):177-199, November 2001.
• Book Review: Data Mining Using Grammar-based Genetic Programming and Applications. AA Freitas. Genetic Programming and Evolvable Machines, 2(2):197-199, June 2001.
• Understanding the crucial differences between classification and discovery of association rules - a position paper. AA Freitas. ACM SIGKDD Explorations, 2(1):65-69, 2000.
• Uma revisao de abordagens geneticodifusas para descoberta de conhecimento em banco de dados. W Romao, AA Freitas, and RCS Pacheco. Acta Scientiarum, 22(5):1347-1359, December 2000.
• Genetic programming for knowledge discovery in chest pain diagnosis. CC Bojarczuk, HS Lopes, and AA Freitas. IEEE Engineering in Medicine and Biology Magazine, 19(4):38-44, July 2000.
• Data Mining with Evolutionary Algorithms: Research Directions. AA Freitas. AI Magazine, 21(1):97, April 2000.
• Interfacing knowledge discovery algorithms to large database management systems. S Lavington, N Dewhurst, E Wilkins, and AA Freitas. Information and Software Technology - special issue on data mining, 41(1999):605-617, 1999.
• On Rule Interestingness Measures. AA Freitas. Knowledge-Based Systems, 12(5-6):309-315, October 1999.

Book Chapters

• Creating rule ensembles from automatically-evolved rule induction algorithms. G.L. Pappa and A.A. Freitas. In Advances in Machine Learning I: Dedicated to the memory of Prof. Ryszard S. Michalski, pages 257-273. Springer, July 2010.
• A tutorial on multi-label classification techniques. A.C.P.L.F. de Carvalho and A.A. Freitas, volume Foundations of Computational Intelligence Vol. 5 of Studies in Computational Intelligence 205, pages 177-195. Springer, September 2009.
• A hybrid rule-induction/likelihood-ratio based approach for predicting protein-protein interactions. M. Iqbal, A.A. Freitas, and C.G. Johnson. In Christine L. Mumford and Lakhmi C. Jain, editors, Computational Intelligence: Collaboration, Fusion and Emergence, Intelligent Systems Reference Library, pages 623-637. Springer, July 2009.
• An empirical evaluation of the effectiveness of different types of predictor attributes in protein function prediction.. F. Otero, M. Segond, A.A. Freitas, C.G. Johnson, D. Robilliard, and C. Fonlupt, volume Foundations of Computational Intelligence Vol. 5 of Studies in Computational Intelligence 205, pages 339-357. Springer, June 2009.
• Ant colony algorithms for data classification. A.A. Freitas, R.S. Parpinelli, and H.S. Lopes. In M. Khosrow-Pour, editor, Encyclopedia of Information Science and Technology, pages 154-159. Information Science Reference, 2nd edition, December 2008.
• Genetic programming for automatically constructing data mining algorithms. A.A. Freitas and G.L. Pappa. In J. wang, editor, Encyclopedia of Data Warehousing and Mining, pages 932-936. Information Science Reference, 2nd edition, December 2008.
• Artificial immune systems in bioinformatics. V. Bevilacqua, F. Menolascina, R.T. Alves, S. Tommasi, G. Mastronardi, M. Delgado, A. Paradiso, G. Nicosia, and A.A. Freitas. In T.G. Smolinski, M.G. Milanova, and A.-E. Hassanien, editors, Computational Intelligence in Biomedicine and Bioinformatics: current trends and applications, pages 271-296. Springer, October 2008.
• A review of evolutionary algorithms for data mining. A.A. Freitas. In O. Maimon and L. Rokach, editors, Soft Computing for Knowledge Discovery and Data Mining, pages 61-93. Springer, November 2007.
• Discovering new rule induction algorithms with grammar-based genetic programming. G.L. Pappa and A.A. Freitas. In O. Maimon and L. Rokach, editors, Soft Computing for Knowledge Discovery and Data Mining, pages 177-196. Springer, November 2007.
• Integrating bayesian networks and simpson’s paradox in data mining. A.A. Freitas, K. McGarry, and E.S. Correa. In F. Russo and J. Williamson, editors, Causality and Probability in the Sciences, pages 43-62. College Publications, January 2007.
• A tutorial on hierarchical classification with applications in bioinformatics.. A.A. Freitas and Andre C.P.F.L. de Carvalho, volume Research and Trends in Data Mining Technologies and Applications, chapter VII, pages 175-208. Idea Group, January 2007.
• Discovering knowledge nuggets with a genetic algorithm. E. Noda and A.A. Freitas, volume Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques, chapter 12, pages 395-432. Springer, July 2006.
• Evolutionary algorithms for data mining. A.A. Freitas, volume The Data Mining and Knowledge Discovery Handbook, pages 435-467. Springer, January 2005.
• Classification-rule discovery with an ant colony algorithm.. R.S. Parpinelli, H.S. Lopes, and A.A. Freitas. In M. Khosrow-Pour, editor, Encyclopedia of Information Science and Technology, pages 420-424. Idea Group, Hershey, January 2005.
• Towards a danger theory inspired artificial immune system for web mining. Andrew Secker, Alex Freitas, and Jon Timmis. In A Scime, editor, Web Mining: applications and techniques, pages 145-168. Idea Group, January 2005.
• Multi-objective algorithms for attribute selection in data mining. G.L. Pappa, A. A. Freitas, and C. A. A. Kaestner. In C.A. Coello Coello and G.B. Lamont, editors, Applications of Multi-Objective Evolutionary Algorithms, pages 603-626. World Scientific, December 2004.
• Multi-objective algorithms for attribute selection in data mining. GL Pappa, AA Freitas, and CAA Kaestner. In C.A. Coello Coello and G.B. Lamont, editors, Applications of Multi-Objective Evolutionary Algorithms, pages 603-626. World Scientific, December 2004.
• A Review of Evolutionary Algorithms for E-Commerce. AA Freitas. In J Segovia, PS Szczepaniak, and M Niedzwiedzinski, editors, E-Commerce and Intelligent Methods. Studies in Fuzziness and Soft Computing, volume 105. Springer-Verlag, Heidelberg, Berlin, 2002.
• An Ant Colony Algorithm for Classification Rule Discovery. RS Parpinelli, HS Lopes, and AA Freitas. In HA Abbass, RA Sarker, and CS Newton, editors, Data Mining: a Heurstic Approach, pages 191-208. Idea Group Publishing, London, 2002.
• Evolutionary computation. AA Freitas. In W Klosgen and J Zytkow, editors, Handbook of Data Mining and Knowledge Discovery. Oxford University Press, August 2002.
• A survey of evolutionary algorithms for data mining and knowledge discovery. AA Freitas. In A Ghosh and S Tsutsui, editors, Advances in Evolutionary Computation, pages 819-845. Springer-Verlag, August 2002.

Conference Papers

• Towards the automatic design of decision tree induction algorithms.. R.C. Barros, A.C.P.L.F. de Carvalho, M.P. Basgalupp, and A.A. Freitas. In Proceedings of the GECCO-2011 First Workshop on Evolutionary Algorithms for Evolving Generic Algorithms, pages 567-574. ACM Press, July 2011.
• A hierarchical approach to represent relational data applied to clustering tasks. J.C. Xavier, A.M.P. Canuto, A.A. Freitas, L.M.G. Goncalves, and C.N. Silla J. In Proceedings of the 2011 International Joint Conference on Neural Networks, pages 3055-3062. IEEE Press, July 2011.
• Implementing a data mining approach to episodic memory modelling for artificial companions. M.U. Keysermann, A.A. Freitas, and P.A. Vargas. In D. Kazakov and G. Tsoulas, editors, Proceedings of AISB’11: Human Memory for Artificial Agents, pages 41-48. Society for the Study of Artificial Intelligence and the Simulation of Behaviour, April 2011.
• Knowledge discovery with artificial immune systems for hierarchical multi-label classification of protein functions. R.T. Alves, M.R. Delgado, and A.A. Freitas. In P. Sobrevilla, J. Aranda, and S. Xambo, editors, Proceedings of the 2010 World Congress on Computational Intelligence (WCII/FUZZ-IEEE 2010), pages 2098-2105, July 2010.
• Web log data clustering for a multi-agent recommendation system. J.C. Xavier, A.A. Freitas, A.M.P. Canuto, and L.M.G. Goncalves. In Machine Learning and Cybernetics (ICMLC): 2010 International Conference on, pages 471-476, July 2010.
• Evolutionary model tree induction. R.C. Barros, M.P. Basgalupp, D.D. Ruiz, A.C.P.L.F. de Carvalho, and A.A. Freitas. In D. Shin, editor, Applied Computing 2010: Proc. 25th Annual ACM Symposium on Applied Computing (SAC-2010), volume Vol. 2, pages 1131-1137. ACM Press, March 2010.
• A global-model naive bayes approach to the hierarchical prediction of protein functions. C.N. Silla Jr. and A.A. Freitas. In W. Wang, H. Kargupta, S. Ranka, P.S. Yu, and X. Wu, editors, Proc. Ninth IEEE Int. Conf. on Data Mining (ICDM-2009), pages 992-997. IEEE Press, December 2009.
• A genetic programming-based tool for protein classification. D.F. Tsunoda, A.A. Freitas, and H.S. Lopes. In A. Abraham, J.M. Benitez, F. Herrera, V. Loia, F. Marcelloni, and S. Senatore, editors, Proc. 9th Int. Conf. on Intelligent System Design and Applications (ISDA’09), pages 1136-1142, December 2009.
• Novel top-down approaches for hierarchical classification and their application to automatic music genre classification. C.N. Silla Jr. and A.A. Freitas. In C.L.P. Chen and R. Roberts, editors, Proc. 2009 IEEE Int. Conf. on Systems, Man and Cybernetics (SMC-2009), pages 3499-3504. IEEE Press, October 2009.
• A hybrid evolutionary approach for the protein classification problem. D.F. Tsunoda, H.S. Lopes, and A.A. Freitas. In N.T. Nguyen, R. Kowalczyk, and S.-M. Chen, editors, Proc. 1st Int. Conf. on Collective Intelligence ( ICCI 2009), pages 629-640, October 2009.
• A hybrid data mining metaheuristic for the p-median problem. A. Plastino, E.R. Fonseca, R. Fuchshuber, S.L. Martins, A.A. Freitas, M. Luis, and S. Salhi. In H. Park, S. Parthasarathy, H. Liu, and Z. Obradovic, editors, Proc. of the Ninth SIAM International Conference on Data Mining (SDM-2009), pages 305-316. SIAM, April 2009.
• Legal-tree: a lexicographic multi-objective genetic algorithm for decision tree induction. M.P. Basgalupp, R.C. Barros, A.C.P.L.F. de Carvalho, A.A. Freitas, and D.D. Ruiz. In S.Y. Shin, S. Ossowski, P. Martins, R. Menezes, M. Virol, J. Hong, D. Shin, M.J. Palakal, U. Fritzke, M. Crosby, and H.M. Haddad, editors, Proceedings of the 2009 ACM Symposium on Applied Computing, pages 1085-1090. ACM Press, March 2009.
• Handling continuous attributes in ant colony classification algorithms. F.E.B. Otero, A.A. Freitas, and C.G. Johnson. In Proc. of the 2009 IEEE Symposium on Computational Intelligence in Data Mining (CIDM 2009), pages 225-231. IEEE Press, March 2009.
• A hierarchical classification ant colony algorithm for predicting gene ontology terms. F.E.B. Otero, A.A. Freitas, and C.G. Johnson. In C. Pizzuti, M.D. Ritchie, and M. Giacobini, editors, Proc. 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2009), volume Lecture Notes in Computer Science 5483, pages 68-79. Springer, March 2009.
• cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes. F.E.B. Otero, A.A. Freitas, and C.G. Johnson. In M. Dorigo et al., editor, Ant Colony Optimization and Swarm Intelligence (Proc. ANTS 2008), LNCS 5217, pages 48-59. Springer, September 2008.
• Multi-label hierarchical classification of protein functions with artificial immune systems. R.T. Alves, M.R. Delgado, and A.A. Freitas. In A.L.C. Bazzan, M. Craven, and N.F. Martins, editors, Advances in Bioinformatics and Computational Biology (Proc. 2008 Brazilian Symposium in Bioinformatics (BSB-2008)), Lecture Notes in Bioinformatics 5167, pages 1-12. Springer, August 2008.
• Top-down hierarchical ensembles of classifiers for predicting g-protein-coupled-receptor functions. E.P. Costa, A.C. Lorena, A.C.P.L.F. Carvalho, and A.A. Freitas. In A.L.C. Bazzan, M. Craven, and N.F. Martins, editors, Advances in Bioinformatics and Computational Biology (Proc. 2008 Brazilian Symposium in Bioinformatics (BSB-2008)), Lecture Notes in Bioinformatics 5167, pages 35-46. Springer, August 2008.
• An artificial immune system for evolving amino acid clusters tailored to protein function prediction. A. Secker, M.N. Davies, A.A. Freitas, J. Timmis, E. Clark, and D.R. Flower. In P.J. Bentley, D. Lee, and S. Jung, editors, Proc. 2008 Int. Conf. on Artificial Immune Systems (ICARIS-2008). Lecture Notes in Computer Science 5132, pages 242-253. Springer, August 2008.
• Autonomously detecting the defective pixels in an imaging sensor array using a robust statistical technique. S. Ghosh, I. Marshall, and A.A. Freitas. In Image Quality and Systems Performance V – Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 6808, page 12 pages, April 2008.
• Improving the performance of hierarchical classification with swarm intelligence. N. Holden and A.A. Freitas. In E. Marchiori and J.H. Moore, editors, Proc. Sixth European Conf. on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2008), Lecture Notes in Computer Science 4973, pages 48-60. Springer, March 2008.
• Protein interaction inference using particle swarm optimization algorithm.. M. Iqbal, A.A. Freitas, and C.G. Johnson. In E. Marchiori and J.H. Moore, editors, Proc. Sixth European Conf. on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBio-2008), Lecture Notes in Computer Science 4973, pages 61-70. Springer, March 2008.
• Robust Autonomous Detection of the Faulty Sensors of a Sensor Array. Siddhartha Ghosh, Ian Marshall, and Alex Freitas. In 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2007, CAMPSAP 2007. IEEE Signal Processing Society, December 2007.
• Discovering multi-label hierarchical classification rules for protein function prediction. R. Alves, M. Delgado, F. Camargo, E. Benelli, and A.A. Freitas. In A. Plastino, A.P.L.F. de Carvalho, R. Ramos, and W.M. Junior, editors, Proc. II Workshop em Algoritmos e Aplicacoes de Mineracao de Dados (Workshop on Algorithms and Applications of Data Mining), pages 87-90. Sociedade Brasileira de Computacao, October 2007.
• An evolutionary density and grid-based clustering algorithm.. C.S. de Oliveira, A.S.G. Meiguins, B.S. Meiguins, P.I. Godinho, and A.A. Freitas. In A.S. da Silva, V.G. Soares, and G. Elias, editors, Proc. of the XXIII Brazilian Symposium on Databases (SBBD-2007), pages 175-189. Sociedade Brasileira de Computacao, October 2007.
• WAIRS: Improving classification accuracy by weighting attributes in the AIRS classifier. Andrew Secker and Alex A. Freitas. In proceedings of the 2007 IEEE Congress on Evolutionary Computation (CEC 2007), pages 3759-3765, Singapore, September 2007. IEEE press.
• Comparing several approaches for hierarchical classification of proteins with decision trees. E.P. Costa, A.C. Lorena, A.C.P.L.F. Carvalho, A.A. Freitas, and H. Holden. In M.-F. Sagot and M.E.M.T. Walter, editors, Advances in Bioinformatics and Computational Biology (Proc. of the Second Brazilian Symposium on Bioinformatics, BSB-2007), Lecture Notes in Bioinformatics 4643, pages 126-137. Springer, August 2007.
• Particle swarm and bayesian networks applied to attribute selection for protein functional classification.. E.S. Correa, A.A. Freitas, and C.G. Johnson. In T. Yu, editor, Proc. of the GECCO-2007 Workshop on Particle Swarms: The Second Decade, pages 2651-2658. ACM, July 2007.
• A review of performance evaluation measures for hierarchical classifiers.. E.P. Costa, A.C. Lorena, A.C.P.L.F. Carvalho, and A.A. Freitas. In C. Drummond, W. Elazmeh, N. Japkowicz, and S.A. Macskassy, editors, Evaluation Methods for Machine Learning II: papers from the AAAI-2007 Workshop, AAAI Technical Report WS-07-05, pages 1-6. AAAI Press, July 2007.
• A hybrid pso/aco algorithm for classification. N. Holden and A.A. Freitas. In T. Yu, editor, Proc. of the GECCO-2007 Workshop on Particle Swarms: The Second Decade, pages 2745-2750. ACM Press, July 2007.
• An experimental comparison of classification algorithms for the hierarchical prediction of protein function. Andrew Secker, Matthew N. Davies, Alex A. Freitas, Jon Timmis, Miguel Mendao, and Darren R. Flower. In Alex A. Freitas, editor, 3rd UK Data mining and Knowledge Discovery Symposium (UKKDD 2007), pages 13-18, April 2007.
• The integration of heterogeneous biological data using bayesian networks. K. McGarry, Nick Morris, and A.A. Freitas. In R. Ellis, R. Allen, and A. Tuson, editors, Applications and innovations in intelligent systems XIV - Proc. of AI-2006, pages 44-57. Springer, December 2006.
• Estimating photometric redshifts using genetic algorithms. N. Miles, A.A. Freitas, and S. Serjeant. In R. Ellis, R. Allen, and A. Tuson, editors, Applications and innovations in intelligent systems XIV - Proc. of AI-2006, pages 75-87. Springer, December 2006.
• Improving the interpretability of classification rules in sparse bioinformatics datasets. J. Smaldon and A.A. Freitas. In M. Bramer, F. Coenen, and A. Tuson, editors, Research and Development in Intelligent Systems XXIII - Proc. AI-2006, pages 377-381. Springer, December 2006.
• Automatically evolving rule induction algorithms. G. L. Pappa and A. A. Freitas. In Johannes Fuernkranz, Tobias Scheffer, and Myra Spiliopoulou, editors, Proc. of the 17th European Conference on Machine Learning, volume 4212/2006 of Lecture Notes in Computer Science, pages 341-352, Berlin, September 2006. Springer Berlin / Heidelberg.
• A new ant colony algorithm for multi-label classification with applications in bioinformatics. A. Chan and A.A. Freitas. In M. Keijzer, editor, Proc. Genetic and Evolutionary Computation Conference (GECCO-2006), pages 27-34. ACM Press, July 2006.
• A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set.. E.S. Correa, A.A. Freitas, and C.G. Johnson. In M. Keijzer and et. al., editors, Proc. Genetic and Evolutionary Computation Conference (GECCO-2006), pages 35-42. ACM Press, July 2006.
• A new version of the ant-miner algorithm discovering unordered rule sets. J. Smaldon and A.A. Freitas. In M. Keijzer and et. al., editors, Proc. Genetic and Evolutionary Computation Conference (GECCO-2006), pages 43-50. ACM Press, July 2006.
• Hierarchical classification of g-protein-coupled receptors with a pso/aco algorithm. N. Holden and A.A. Freitas. In Proc. IEEE Swarm Intelligence Symposium (SIS-06), pages 77-84. IEEE Press, June 2006.
• Evaluating the correlation between objective rule interestingness measures and real human interest. DR Carvalho, AA Freitas, and N Ebecken. In A. Jorge, L Torgo, P. Brazdil, R. Camacho, and J. Gama, editors, Knowledge Discovery in Databases: Proc. of PKDD-2005. LNAI 3731, pages 453-461. Springer Verlag, October 2005.
• A new classification-rule pruning procedure for an ant colony algorithm. A Chan and AA Freitas. In E-G. Talbi, P. Liardet, P. Collet, E. Lutton, and M. Schoenauer, editors, Artificial Evolution: Proc. 7th Int. Conf. (EA-2005, Lille, France, Oct. 2005), volume Lecture Notes in Computer Science 3871, pages 25-36. Springer, October 2005.
• Varying the topology and probability of re-initialization in particle swarm optimization. Mudassar Iqbal, Alex A. Freitas, and Colin G. Johnson. In E.-G. Talbi, editor, Evolution Artificielle 2005. University of Lille, October 2005.
• Advances in artificial life - proc. 8th european conf. (ecal-2005). MS Capcarrere, AA Freitas, PJ Bentley, CG Johnson, and J Timmis, editors, volume Lecture Notes in Artificial Intelligence 3630. Springer Verlag, September 2005.
• A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data. N Holden and AA Freitas. In P Arabshahi and A Martinoli, editors, Proc. 2005 IEEE Swarm Intelligence Symposium, pages 100-107. IEEE, June 2005.
• An evolutionary approach for motif discovery and transmembrane protein classification. DF Tsunoda, HS Lopes, and AA Freitas. In F Rothlauf and et al., editors, Applications of Evolutionary Computing (Proc. of EvoBIO-2005: 3rd European Workshop on Evolutionary Bioinformatics), LNCS 3449, pages 105-114. Springer, March 2005.
• Using genetic algorithms to mine interesting dependence modeling rules. AS Goncalves, AA Freitas, R Kato, and RCL de Oliveira. In M.H. Hamza, editor, Proc. 23rd IASTED Int. Multi-Conference on Databases and Applications (DBA-2005), pages 1-6. Acta Press, February 2005.
• Induction of fuzzy classification rules with an artificial immune system. R.T. Alves, M.R. Delgado, H.S. Lopes, and A.A. Freitas. In A. Barros, A. Araujo, H.C. Yehia, and R. Teixeira, editors, Proc. 8th Brazilian Symp. on Neural Networks. IEEE Comp Soc Press, November 2004.
• Automatic text summarization with genetic algorithm-based attribute selection. C.N. Silla Jr, G.L. Pappa, A.A. Freitas, and C.A.A. Kaestner. In C. Lemaitre, C.A. Reyes, and J.A. Gonzales, editors, Advances in Artificial Intelligence (IBERAMIA 2004, Proc. 9th Ibero-American Conference on AI), LNCS, volume 3315, pages 305-314. Springer-Verlag, November 2004.
• Automatic text summarization with genetic algorithm-based attribute selection. Carlos N. Silla, Gisele L. Pappa, Alex A. Freitas, and Celso A. A. Kaestner. In Christian Lemaitre, Carlos A. Reyes, and Jesus A. Gonzalez, editors, Advances in Artificial Intelligence – IBERAMIA 2004: 9th Ibero-American Conference on AI, Lecture Notes in Computer Science, volume 3315, pages 305-314, November 2004.
• An artificial immune system for fuzzy-rule induction in data mining. RT Alves, MR Delgado, HS Lopes, and AA Freitas. In Xin Yao and et al, editors, Parallel Problem Solving from Nature - PPSN VIII, LNCS 3242, pages 1011-1020. Springer-Verlag, September 2004.
• Web page classification with an ant colony algorithm. N Holden and AA Freitas. In Xin Yao and et al, editors, Parallel Problem Solving from Nature - PPSN VIII, LNCS 3242, pages 1092-1102. Springer-Verlag, September 2004.
• Towards a genetic programming algorithm for automatically evolving rule induction algorithms. GL Pappa and AA Freitas. In J. Furnkranz, editor, Proc. ECML/PKDD-2004 Workshop on Advances in Inductive Learning, pages 93-108, Pisa, Italy, September 2004.
• Handling inconsistency in distributed data mining with paraconsistent logic. SNM Ferreira, AA Freitas, and BC Avila. In C Guzelis, E Alpaydin, T Yakhno, and F Gurgen, editors, Proc. 13th Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN-2004), pages 19-28, Izmir, Turkey, June 2004.
• A critical review of rule surprisingness measures. DR Carvalho, AA Freitas, and NFF Ebecken. In NFF Ebecken, CA Brebbia, and A Zanasi, editors, Proc. Data Mining IV - Int. Conf. on Data Mining, pages 545-556. WIT Press, December 2003.
• AISEC: An Artificial Immune System for E-mail Classification. A. Secker, A Freitas, and J. Timmis. In R. Sarker, R. Reynolds, H. Abbass, T. Kay-Chen, R. McKay, D Essam, and T. Gedeon, editors, Proceedings of the Congress on Evolutionary Computation, pages 131-139, Canberra. Australia, December 2003. IEEE.
• A non-linear topic detection method for text summarization using wordnet. CN Silla Jr, CAA Kaestner, and AA Freitas. In MGV Nunes, SM Aluisio, LHM Oliveira, and JA Teles, editors, Proc. I Workshop em Tecnologia da Informacao e Linguagem Humana. ICMC-USP, Brazil, October 2003.
• Revisiting the Foundations of Artificial Immune Systems: A Problem Oriented Perspective. A Freitas and J Timmis. In J. Timmis, P. Bentley, and E. Hart, editors, Proceedings of the 2nd International Conference on Artificial Immune Systems, volume 2787 of Lecture Notes in Computer Science, pages 229-241. Springer, September 2003.
• A Danger Theory Approach to Web Mining. A. Secker, A Freitas, and J. Timmis. In J. Timmis, P. Bentley, and E. Hart, editors, Proceedings of the 2nd International Conference on Artificial Immune Systems, volume 2787 of Lecture Notes in Computer Science, pages 156-167. Springer, September 2003.
• An innovative application of a constrained-syntax genetic programming system to the problem of predicting survival of patients.. CC Bojarczuk, HS Lopes, and AA Freitas. In C. Ryan, M. Keijzer, R. Poli, T. Soule, E. Tsang, and E. Costa, editors, Genetic Programming: Proc. 6th European Conference (EuroGP-2003), volume 2610 of Lecture Notes in Computer Science. Springer-Verlag, April 2003.
• Genetic Programming for Attribute Construction in Data Mining. FEB Otero, MMS Silva, AA Freitas, and JC NIevola. In C. Ryan, M. Keijzer, R. Poli, T. Soule, E. Tsang, and E. Costa, editors, Genetic Programming: Proc. 6th European Conference (EuroGP-2003)., volume 2610 of Lecture Notes in Computer Science, pages 384-393. Springer-Verlag, April 2003.
• New results for a hybrid decision tree/genetic algorithm for data mining. DR Carvalho and AA Freitas. In A. Lofti, J. Garibaldi, and R. John, editors, Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), pages 260-265. Nottingham Trent University, December 2002.
• Constructing x-of-n attributes with a genetic algorithm. O Larsen, AA Freitas, and JC Nievola. In A. Lofti, J. Garibaldi, and R. John, editors, Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), pages 326-331. Nottingham Trent University, December 2002.
• A multiobjective genetic algorithm for attribute selection. G. L. Pappa, A. A. Freitas, and C. A. A. Kaestner. In A. Lofti, J. Garibaldi, and R. John, editors, Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), pages 116-121. Nottingham Trent University, December 2002.
• A multiobjective genetic algorithm for attribute selection. GL Pappa, AA Freitas, and CAA Kaestner. In A. Lofti, J. Garibaldi, and R. John, editors, Proc. 4th Int. Conf. on Recent Advances in Soft Computing (RASC-2002), pages 116-121. Nottingham Trent University, December 2002.
• Automatic text summarization using a machine learning approach. J Larroca Neto, AA Freitas, and CAA Kaestner. In G Bittencourt and GL Ramalho, editors, Proc. 16th Brazilian Symp. on Artificial Intelligence (SBIA-2002). Lecture Notes in Artificial Intelligence 2507, pages 205-215. Springer-Verlag, November 2002.
• Attribute selection with a multiobjective genetic algorithm. G.L. Pappa, A. A. Freitas, and C. A. A. Kaestner. In G. Bittencourt and G.L. Ramalho, editors, Proc. 16th Brazilian Symp. on Artificial Intelligence (SBIA-2002), Lecture Notes in Artificial Intelligence 2507, pages 280-290. Springer-Verlag, November 2002.
• Attribute selection with a multiobjective genetic algorithm. GL Pappa, AA Freitas, and CAA Kaestner. In G Bittencourt and GL Ramalho, editors, Proc. 16th Brazilian Symp. on Artificial Intelligence (SBIA-2002). Lecture Notes in Artificial Intelligence 2507, pages 280-290. Springer-Verlag, November 2002.
• Mining comprehensible rules from data with an ant colony algorithm. RS Parpinelli, HS Lopes, and AA Freitas. In G Bittencourt and GL Ramalho, editors, Proc. 16th Brazilian Symp. on Artificial Intelligence (SBIA-2002). Lecture Notes in Artificial Intelligence 2507, pages 259-269. Springer-Verlag, November 2002.
• A distributed-population genetic algorithm for discovering interesting prediction rules. Edgar Noda, Alex A. Freitas, and Akebo Yamakami. In Jose M. Benitez and Oscar Gordon, editors, 7th Online World Conference on Soft Computing in Industrial Applications (WSC7), page 8. University of Granada, Spain, September 2002.
• A genetic algorithm with sequential niching for discovering small-disjunct rules. DR Carvalho and AA Freitas. In WB Langdon, E Cantu-Paz, and et al, editors, Proceedings Genetic and Evolutionary Computation Conference (GECCO-2002), pages 1035-1042, New York and San Francisco, USA, July 2002. Morgan Kaufmann.
• Constructing X-of-N Attributes with a Genetic Algorithm. O Larsen, AA Freitas, and JC Nievola. In Proc Genetic and Evolutionary Computation Conf (GECCO-2002), page 1268, New York, July 2002. Morgan Kaufmann, San Francisco.
• Genetic Programming for Attribute Construction in Data Mining. FEB Otero, MMS Silva, and AA Freitas. In Proc Genetic and Evolutionary Computation Conf (GECCO-2002), page 1270, New York, July 2002. Morgan Kaufmann, San Francisco.
• A Genetic Algorithm for Discovering Interesting Fuzzy Prediction Rules: applications to science and technology data. W Romao, AA Freitas, and PCS Pacheco. In WB Langdon, E Cantu-Paz, and et al, editors, Proceedings of Genetic and Evolutionary Computation Conference (GECCO-2002), pages 1188-1195, San Francisco, CA, USA, July 2002. Morgan Kaufmann.
• Discovering fuzzy classification rules with genetic programming and co-evolution. RRF Mendes, FB Voznika, AA Freitas, and JC Nievola. In Principles of Data Mining and Knowledge Discovery (Proc. 5th European Conference PKDD 2001) - Lecture Notes in Artificial Intelligence, 2168, pages 314-325, Berlin, 2001. Springer-Verlag.
• Incorporating deviation-detection functionality into the OLAP paradigm. CC Fabris and AA Freitas. In MLQ Mattoso and G Xexeo, editors, Proc. XVI Brazilian Symposium on Databases (SBBD-2001), pages 274-285, Rio de Janeiro, Brazil, October 2001.
• Data Mining with Constrained-syntax Genetic Programming: Applications in Medical Data Sets. CC Bojarczuk, HS Lopes, and AA Freitas. In Proc Intelligent Data Analysis in Medicine and Pharmacology - a workshop at MedInfo-2001, London, September 2001.
• An Immunological Algorithm for Discovering Small-disjunct Rules in Data Mining. DR Carvalho and AA Freitas. In Proc Graduate Student Workshop at GECCO-2001, pages 401-404, San Francisco, USA, July 2001.
• A Genetic Algorithm for the P-median Problem. ES Correa, MTA Steiner, AA Freitas, and C Carnieri. In LE Spector, E Goodman, and et al, editors, Proc. 2001 Genetic and Evolutionary Computation Conference (GECCO-2001), pages 1268-1275, San Fracisco, USA, July 2001. Morgan Kaufmann.
• An ant colony based system for data mining: applications to medical data. RS Parpinelli, HS Lopes, and AA Freitas. In LE Spector, E Goodman, and et al, editors, Proc. 2001 Genetic and Evolutionary Computation Conference (GECCO-2001), pages 791-798, San Francisco, USA, July 2001. Morgan Kaufmann.
• A Genetic Algorithm-based Solution for the Problem of Small Disjuncts. DE Carvalho and AA Freitas. In DA Zighed, J Komorowski, and J Zytkow, editors, Principles of Data Mining and Knowledge Discovery (Proc. 4th European Conf. PKDD-2000, Lyon, France), volume 1910 of Lecture Notes in Artificial Intelligence, pages 345-352, Berlin, 2000. Springer-Verlag.
• Document Clustering and Text Summarization. J Larocca Neto, AD Santos, CAA Kaestner, and AA Freitas. In N Mackin, editor, Proc. 4th International Conference Practical Applications of Knowledge Discovery and Data Mining (PADD-2000), pages 41-55, London, 2000. The Practical Application Company.
• Sumarizacao de textos usando algoritmos de classificacao. FEB Otero and AA Freitas. In E Lethelier, F Bortolozzi, KC Weber, and H Pereira, editors, Proc. 2000 Int. Symp. on Knowledge Management/Document Management (ISKM/DM-2000), pages 347-357, Curitiba, Brazil, 2000. Editora Universitaria Champagnat (PUCPR).
• Generating Text Summaries through the Relative Importance of Topics. J Larocca Neto, AD Santos, CAA Kaestner, and AA Freitas. In Proc. Int. Joint Conf. IBERAMIA-2000 (7th Ibero-American Conf. on Artif. Intel.) and SBIA-2000 (15th Brazilian Symp. on Artif. Intel.), volume 1952 of Lecture Notes in Artificial Intelligence, pages 301-309, Atibaia, SP, Brazil, November 2000. Springer-Verlag.
• A Trainable Algorithm for Summarizing News Stories. J Larocca Neto, AD Santos, CAA Kaestner, AA Freitas, and JC Nievola. In H Zaragoza, P Gallinari, and M Rajman, editors, Proc. PKDD'2000 Workshop on Machine Learning and Textual Information Access, Lyon, France, September 2000.
• Rule Discovery with a Parallel Genetic Algorithm. DLA Araujo, HS Lopes, and AA Freitas. In Proc 2000 Genetic and Evolutionary Computation Conf Workshop Program, pages 89-92, Las Vegas, USA, July 2000.
• A hybrid decision tree/genetic algorithm for coping with the problem of small disjuncts in data mining. DR Carvalho and AA Freitas. In Proc. Genetic and Evolutionary Computation Conf (GECCO-2000), pages 1061-1068, Las Vegas, USA, July 2000. Morgan Kaufmann.
• Discovering comprehensible classification rules with a genetic algorithm. MV Fidelis, HS Lopes, and AA Freitas. In Proc. Congress on Evolutionary Computation (CEC-2000), pages 805-810, La Jolla, CA, USA, July 2000. IEEE.
• The integrated data mining tool MineKit and a case study of its application on video shop data. J Larocca Neto, AD Santos, CAA Kaestner, and AA Freitas. In C Fyfe, editor, Proc. 2nd Int. ICSC Symp. on Engineering of Intelligent Systems (EIS-2000), Scotland, July 2000. ICSC Academic Press.
• Comparing a genetic algorithm with a rule induction algorithm in the data mining task of dependence modeling. E Noda, AA Freitas, and HS Lopes. In Proc 2000 Genetic and Evolutionary Computation Conf (GECCO-2000), page 1080, Las Vegas, USA, July 2000.
• Extracting comprehensible rules from neural networks via genetic algorithms. R Santos, JC Nievola, and AA Freitas. In Proc. 2000 IEEE Symp. on Combinations of Evolutionary Computation and Neural Networks (ECNN-2000), pages 130-139, San Antonio, TX, USA, May 2000. IEEE.
• Discovering surprising patterns by detecting occurrences of Simpson's paradox. CC Fabris and AA Freitas. In M Bramer, A Macintosh, and F Coenen, editors, Research and Development in Intelligent Systems XVI, (Proc 19th SGES Int Conf on Knowledge Based Systems and Applied Artificial Intelligence), pages 148-160, Berlin, 1999. Springer-Verlag.
• A Fuzzy Beam-Search Rule Induction Algorithm. CS Fertig, AA Freitas, LVR Arruda, and C Kaestner. In J Zytkow and J Rauch, editors, Principles of Data Mining and Knowledge Discovery (Proc 3rd European Conf - PKDD-99), volume 1704 of Lecture Notes in Artificial Intelligence, pages 341-347, Berlin, 1999. Springer-Verlag.
• A genetic algorithm for generalized rule induction. AA Freitas. In R Roy, T Furuhashi, and PK Chawdhry, editors, Advances in Soft Computing - Engineering Design and Manufacturing (Proc WSC3 3rd on-line world conf hosted on the internet 1998), pages 340-353, Berlin, 1999. Springer-Verlag.
• Data Mining with Evolutionary Algorithms: Research Directions - Papers from the AAAI Workshop. AA Freitas. Number WS-99-06, Menlo Park, CA, USA, 1999. AAAI Press.
• A Parallel Genetic Algorithm for Rule Discovery in Large Databases. DLA Araujo, HS Lopes, and AA Freitas. In K Ilto, editor, Proc 1000 IEEE Systems, Man and Cybernetics Conf, volume III, pages 940-945, Tokyo, October 1999. IEEE.
• A method for the optimum solution of the permutational flowshop sequencing problem with fuzzy processing times. MA Visintin, AA Freitas, and LVR Arruda. In MF Carvalho and FM Muller, editors, Proc 15th Int Conf on CAD/CAM Robotics and Factories of the Future, pages MW2-1/2-6, Brazil, August 1999. Aguas de Lindoia-SP.
• Discovering comprehensible classification rules using genetic programming: a case study in a medical domain. CC Bojarczuk, HS Lopes, and AA Freitas. In W Banzhaf and J Daida et al, editors, Proc Genetic and Evolutionary Computation Conference (GECCO-99), pages 953-958, Orlando USA, July 1999. Morgan Kaufmann.
• A summary of the papers presented at the AAAI-99 and GECCO-99 Workshop on Data Mining with Evolutionary Algorithms: Research Directions. AA Freitas. In Proc 1999 Genetic and Evolutionary Computation Conf (GECCO-99), page 226, Orlando, USA, July 1999.
• Discovering Interesting Prediction Rules with a Genetic Algorithm. E Noda, AA Freitas, and HS Lopes. In P Angeline, editor, Proc Conference on Evolutionary Computation (CEC-99), pages 1322-1329, Washington DC, USA, July 1999. IEEE.
• Extracao de regras de redes neurais via algoritmos geneticos. RT Santos, JC Nievola, AA Freitas, and HS Lopes. In Proc IV Brazilian Conf on Neural Networks, pages 158-163. Sao Jose dos Camps - SP, Brazil, July 1999.
• A hybrid genetic algorithm/decision tree approach for coping with unbalanced classes. DR Carvalho, BC Avila, and AA Freitas. In N Mackin, editor, Proc 3rd Int Conf on the Practical Applications of Knowledge Discovery and Data Mining (PADD-99), pages 61-70, London, April 1999. The Practical Application Company.
• A Survey of Parallel Data Mining. AA Freitas. In HF Arner and N Mackin, editors, Proc 2nd Int Conf on the Practical Applications of Knowledge Discovery and Data Mining, pages 287-300, London, 1998. The Practical Application Company.
• Um metodo para solucao exata do problema flowshop permutacional. MA Visintin and AA Freitas. In Anais do XXX SBPO (Simposio Brasileiro de Pesquisa Operacional), pages 311-312, Curitiba - PR, Brazil: PUC-PR, November 1998.
• A Multi-criteria approach for the evaluation of rule interestingness. AA Freitas. In N Ebecken, editor, Data Mining (Proc Int Conf Rio de Janeiro, Brazil), pages 7-20, Boston, September 1998. WIT Press.
• The Principle of Transformation Between Efficiency and Effectiveness: Towards a Fair Evaluation of the Cost-Effectivenss of KDD Techniques. AA Freitas. In J Komorowski and J Zytkow, editors, Principles of Data Mining and Knowledge Discovery (Proc 1st European Symp KPKDD'97, Trondheim, Norway, LNAI 1263, pages 299-306, Berlin, 1997. Springer-Verlag.
• A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction. AA Freitas. In JR Koza and K Deb et al, editors, Genetic Programming 1997: Proc 2nd Annual Conf, pages 96-101. Stanford University, CA, USA, Morgan Kaufmann, July 1997.
• Towards Large-Scale Knowledge Discovery in Databases (KDD) by Exploiting Parallelism in Generic KDD Primitives. AA Freitas. In P Shoval and A Silberschatz, editors, Proc 3rd Int Workshop on Next-Generation Information Technologies and Systems, pages 33-43, Neve Ilan, Israel, July 1997.
• The trade-off between effectiveness and efficiency in attribute selection: in defense of the filter approach. AA Freitas. In R Ng, editor, Proc 1997 SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, page 91, Technical Report 97-07, May 1997.
• Parallel Data Mining for Very Large Relational Databases. AA Freitas and SH Lavington. In H Liddel et al, editor, Proc Int Conf on High-Performance Computing and Networking (HPCN'96), Lecture Notes in Computer Science, 1067, pages 158-163, Berlin, 1996. Springer-Verlag.
• Speeding up knowledge discovery in large relational databases by means of a new discretization algorithm. AA Freitas and SH Lavington. In R Morrison and J Kennedy, editors, Advances in Databases (Proc 14th British Nat Conf on Databases, Edinburgh, UK, number 1094 in Lecture Notes in Computer Science, pages 124-133, Berlin, 1996. Springer-Verlag.
• A Framework for Data-parallel Knowledge Discovery in Databases. AA Freitas and SH Lavington. In IEE Colloquium on Knowledge Discovery and Data Mining: Digest No 96/198, pages 6/1-6/4, London, October 1996. IEE.
• Using SQL primitives and parallel DB servers to speed up knowledge discovery in large relational databases. AA Freitas and SH Lavington. In R Trappl, editor, Cybernetics and Systems '96: Proc 13th European Meeting on Cybernetics and Systems Research, pages 955-960, Vienna, Austria, April 1996.
• Applying Genetic Algorithms to the Load-Balancing Problem. AA Freitas, JC Anacleto, and C Kirner. In R Baeza-Yates, editor, Computer Science 2: Research and Applications (Proc 13th Int Conf Chilean Computer Society), pages 7-13, New York: Plenum, 1994.
• Algoritmos Geneticos e sua aplicacao ao problema do corte de barras. AA Freitas, JC Anacleto, R Morabito Neto, and C Kirner. In Proc I Simp Brasileiro em Automacao Inteligente, pages 38-47, Rio Claro - SP, Brazil: UNESP, August 1993.
• Fine-grain parallel best-first branch-and-bound. AA Freitas and C Kirner. In Proc XIX Latin-American Conf on Informatics, pages 421-436, Buenos Aires, Argentina, August 1993.
• Tailoring A* for a massively parallel machine: application to the traveling salesman problem. AA Freitas and C Kirner. In Proc I Simp Brasileiro em Automacao Inteligente, pages 371-380, Rio Claro - SP< Brazil: UNESP, August 1993.
• Algoritmos para descobrir o maximo em um multicomputador baseado em barramentos hierarquicos. AA Freitas and C Kirner. In Proc IV Simp Brasileiro em Arquitetura de Computadores, pages 289-303, Sao Paulo - SP, Brazil: USP, October 1992.
• Introducao a algoritmos geneticos. AA Freitas and C Kirner. In Proc Workshop em Redes Neurais, pages 71-88, Sao Carlos - SP, Brazil: UFSCar, September 1992.

Miscellaneous

• Special issue on data mining and knowledge discovery with evolutionary algorithms. A Ghosh and AA Freitas. IEEE Trans. on Evolutionary Computation 7(6), pp. 517-575, December 2003.