Journal Articles

• Hillard, D., Manavoglu, E., Raghavan, H., Leggetter, C., Cantú-Paz, E. Iyer, R. (2011). The sum of its parts: reducing sparsity in click estimation with query segments. Information Retrieval. 14 (3), 315-336.
• Hidalgo, I., Fernandez, F., Lanchares, J., Cantú-Paz, E., Zomaya, A. (2010). Parallel architectures and bioinspired algorithms. Parallel Computing. 36 (10), 553--554.
• Cantú-Paz, E. and Kamath, C. (2005). An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems. IEEE Transactions on System, Man, and Cybernetics, Part B. 35(5), 915--927.
• Cantú-Paz, E. and Kamath, C. (2003). Evolving neural networks to identify bent-double galaxies in the FIRST survey. Neural Networks. 16(3-4), 507--517.
• Cantú-Paz, E. and Kamath, C. (2003). Inducing oblique decision trees with evolutionary algorithms. IEEE Transactions on Evolutionary Computation. 7(1), 54--68.
• Cantú-Paz, E. (2002). Order statistics and selection methods of evolutionary algorithms. Information Processing Letters. 82(1), 15--22.
• Cantú-Paz, E. (2001). Migration policies, selection pressure, and parallel evolutionary algorithms. Journal of Heuristics, 7(4), 311--334.
• Cantú-Paz, E. (2000). Markov chain models of parallel genetic algorithms. IEEE Transactions on Evolutionary Computation, 4(3), 216--226.
• Cantú-Paz, E. and Goldberg, D.E. (2000). Parallel genetic algorithms: theory and practice. Computer Methods in Applied Mechanics and Engineering, 186, 221--238.
• Pelikan, M., Goldberg, D.E., and Cantú-Paz, E. (2000). Linkage problem, distribution estimation, and Bayesian networks. Evolutionary Computation, 8(3), 311--340.
• Cantú-Paz, E. and Goldberg, D.E. (1999). On the scalability of parallel genetic algorithms. Evolutionary Computation, 7(4), 429--449.
• Harik, G., Cantú-Paz, E., Goldberg, D.E., and Miller, B. (1999). The gambler's ruin problem, genetic algorithms, and the sizing of populations. Evolutionary Computation, 7(3), 231--253.
• Cantú-Paz, E. (1998). A survey of parallel genetic algorithms. Calculateurs Parallèles, Reseaux et Systems Repartis, 10(2), 141--171.

Refereed Conferences

• Xu, W., Manavoglu, E., Cantú-Paz, E. (2010). Temporal click model for sponsored search. Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. (pp. 106--113).
• Cheng, H., Cantú-Paz, E. (2010). Personalized click prediction in sponsored search. Proceedings of the third ACM International Conference on Web search and Data Mining. (pp. 351--360).
• Cantú-Paz, E., Newsam, S., Kamath, C. (2004). Feature Selection in Scientific Applications. International Conference on Knowledge Discovery and Data Mining. (pp. 788-793). New York: ACM Press.
• Cantú-Paz, E. (2004). Feature Subset Selection, Class Separability, and Genetic Algorithms. In Deb, K. et al. (Eds.), Genetic and Evolutionary Computation Conference -- GECCO-2004. (pp. 959--970). Berlin: Springer Verlag. (One of five nominees for best paper award out of 158 submissions to the GA track.)
• Cantú-Paz, E. (2004). Adaptive Sampling for Noisy Problems. In Deb, K. et al. (Eds.), Genetic and Evolutionary Computation Conference -- GECCO-2004. (pp. 947--958). Berlin: Springer Verlag.
• Fernández de Vega, F., Cantú-Paz, E., López, J.I., Manzano, T. (2004). Saving Resources with Plagues in Genetic Algorithms, In Yao, X. et al. (Eds.) Parallel Problem Solving from Nature---PPSN VIII. (pp. 272--281). Berlin: Springer Verlag.
• Cantú-Paz, E. (2003). Pruning Neural Networks with Distribution Estimation Algorithms. In Cantú-Paz, E. et al. (Eds.), Genetic and Evolutionary Computation Conference -- GECCO-2003. (pp. 790--800). Berlin: Springer Verlag.
• Cantú-Paz, E. and Goldberg, D. E. (2003). Are multiple runs of genetic algorithms better than one? In Cantú-Paz, E. et al. (Eds.), Genetic and Evolutionary Computation Conference -- GECCO-2003. (pp. 801--812). Berlin: Springer Verlag. (One of five nominees for best paper award out of 160 submissions to the GA track.)
• Cantú-Paz, E. (2002). Feature subset selection by estimation of distribution algorithms. In W. B. Langdon et al. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002). (pp. 303--310). San Francisco, CA: Morgan Kaufmann Publishers.
• Cantú-Paz, E. (2002). On random numbers and the performance of genetic algorithms. In W. B. Langdon et al. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002). (pp. 311--318). San Francisco, CA: Morgan Kaufmann Publishers.
• Cantú-Paz, E. and Kamath, C. (2002). Evolving neural networks for the classification of galaxies. In W. B. Langdon et al. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002). (pp. 1019--1026). San Francisco, CA: Morgan Kaufmann Publishers. (Winner of best-paper award, real world applications track)
• Kamath, C., Cantú-Paz, E., and Littau, D. (2002). Approximate Splitting for Ensembles of Trees using Histograms. In Proceedings of the Second SIAM International Conference on Data Mining (SDM'2002).
• Kirshner, S., Cadez, I.V., Smyth, P., Kamath, C., and Cantú-Paz, E. (2002). Probabilistic model-based detection of bent-double radio galaxies. International Conference on Pattern Recognition.
• Cantú-Paz, E. (2001). Single vs. multiple runs under constant computation cost. In L. Spector et al. (Eds.) GECCO-2001: Proceedings of the Genetic and Evolutionary Computation Conference. San Francisco, CA: Morgan Kaufmann. (poster presentation)
• Cantú-Paz, E. (2000). Selection intensity in genetic algorithms with generation gaps. In Whitley, D., Goldberg, D. E., Cantú-Paz, E., Spector, L., Parmee, I., and Beyer, H.-G. (Eds.), GECCO-2000: Proceedings of the Genetic and Evolutionary Computation Conference. (pp. 911--918). San Francisco, CA: Morgan Kaufmann.
• Cantú-Paz, E. and Kamath, C. (2000). Using evolutionary algorithms to induce oblique decision trees. In Whitley, D., Goldberg, D. E., Cantú-Paz, E., Spector, L., Parmee, I., and Beyer, H.-G. (Eds.), GECCO-2000: Proceedings of the Genetic and Evolutionary Computation Conference. (pp. 1053--1060). San Francisco, CA: Morgan Kaufmann.
• Pelikan, M., Goldberg, D.E., and Cantú-Paz, E. (2000). Bayesian Optimization Algorithm, population size, and time to convergence. In Whitley, D., Goldberg, D. E., Cantú-Paz, E., Spector, L., Parmee, I., and Beyer, H.-G. (Eds.), GECCO-2000: Proceedings of the Genetic and Evolutionary Computation Conference. (pp. 275--282). San Francisco, CA: Morgan Kaufmann.
• Cantú-Paz, E. (1999). Migration policies and takeover times in genetic algorithms. In Banzhaf, W., Daida, J., Eiben, A. E., Garzon, M. H., Honavar, V., Jakiela, M., & Smith, R. E. (Eds.), GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. (p. 775). San Francisco, CA: Morgan Kaufmann Publishers.
• Cantú-Paz, E. (1999). Topologies, migration rates, and multi-population parallel genetic algorithms. In Banzhaf, W., Daida, J., Eiben, A. E., Garzon, M. H., Honavar, V., Jakiela, M., & Smith, R. E. (Eds.), GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. (pp. 91--98). San Francisco, CA: Morgan Kaufmann.
• Pelikan, M., Goldberg, D.E., and Cantú-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In Banzhaf, W., Daida, J., Eiben, A. E., Garzon, M. H., Honavar, V., Jakiela, M., & Smith, R. E. (Eds.), GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. (pp. 525--532). San Francisco, CA: Morgan Kaufmann.
• Cantú-Paz, E. (1998). Using Markov chains to analyze a bounding case of parallel genetic algorithms. In Koza, J., Banzhaf, W., Chellapilla, K., Deb, K., Dorigo, M., Fogel, D., Garzon, M., Goldberg, D. E., Iba, H. & Riolo, R. (Eds.), Genetic Programming: Proceedings of the Third Annual Conference. (pp. 456--462). San Francisco, CA: Morgan Kaufmann.
• Cantú-Paz, E. (1998). Designing efficient master-slave parallel genetic algorithms. In Koza, J., Banzhaf, W., Chellapilla, K., Deb, K., Dorigo, M., Fogel, D., Garzon, M., Goldberg, D. E., Iba, H. & Riolo, R. (Eds.), Genetic Programming: Proceedings of the Third Annual Conference. (pp. 455). San Francisco, CA: Morgan Kaufmann. (poster presentation)
• Harik, G., Cantú-Paz, E., Goldberg, D.E., and Miller, B. (1997). The gambler's ruin problem, genetic algorithms, and the sizing of populations. In Bäck, T. (Ed.), Proceedings of the IEEE International Conference on Evolutionary Computation. (pp. 7--12). New York, NY: IEEE Press.
• Cantú-Paz, E. & Goldberg, D. E. (1997). Modeling idealized bounding cases of parallel genetic algorithms. In Koza, J., Deb, K., Dorigo, M., Fogel, D., Garzon, M., Iba, H., & Riolo, R. (Eds.), Genetic Programming 1997: Proceedings of the Second Annual Conference (pp. 353--361). San Francisco, CA: Morgan Kaufmann Publishers.
• Cantú-Paz, E. & Goldberg, D. E. (1997). Predicting speedups of idealized bounding cases of parallel genetic algorithms. In Bäck, T. (Ed.), Proceedings of the Seventh International Conference on Genetic Algorithms (pp. 113--121). San Francisco: Morgan Kaufmann.
• Cantú-Paz, E. and Mejía Olvera, M. (1994). Experimental results on distributed genetic algorithms. In Proceedings of the Second International Symposium on Applied Corporate Computing. (pp. 99--107). Monterrey, México.
• Mejía Olvera, M. and Cantú-Paz, E. (1994) DGENESIS—Software para la ejecución de algoritmos genéticos distribuidos. In Memorias de la XX Conferencia Latinoamericana de Informática (pp. 935--946). Atizapán de Zaragoza, México. (In Spanish)

Book Chapters

• Cantú-Paz, E. (2007). Parameter setting in parallel genetic algorithms. In Lobo, F., Lima, C., and Michalewicz, Z. (Ed.), Paramter Setting in Evolutionary Algorithms, pp. 259--276. Berlin: Springer Verlag.
• Cantú-Paz, E. (2006). Feature subset selection with hybrids of filters and evolutionary algorithms. In Scalable Optimization via Probabilistic Modeling, pp. 291--314. Berlin: Springer Verlag.
• Ocenasek, J., Cantú-Paz, E., Pelikan, M. and J. Schwarz. (2006). Design of parallel estimation of distribution algorithms. In Scalable Optimization via Probabilistic Modeling, pp. 187--203. Berlin: Springer Verlag.
• Cantú-Paz, E. (2005). Theory of Parallel Genetic Algorithms. In Alba, E. (Ed.), Parallel Metaheuristics: A New Class of Algorithms, pp. 425--446. Wiley-Interscience.
• Kamath, C., Cantú-Paz, E., Cheung, S., Fodor, I.K., Tang, N. (2005). Experiences in mining data from computer simulations. In Zurada, J. and Kantardzic, M. (Eds.), New Generation of Data Mining Applications, Wiley-{IEEE} Press.
• Cantú-Paz, E. and Kamath, C. (2002). On the use of evolutionary algorithms in data mining. In Abbass, H., Sarker, R., and Newton, C. (Eds.), Data Mining: a Heuristic Approach, pp. 48--71. Hershey, PA: IDEA Group Publishing.
• Kamath, C., Cantú-Paz, E., Fodor, I., and Tang, N. (2001). Searching for bent-double galaxies in the FIRST survey. In Grossman, R., Kamath, C., Kegelmeyer, W., Kumar, V., Namburu, R. (Eds.), Data Mining for Scientific and Engineering Applications, pp. 95--114. Boston, MA: Kluwer.
• Cantú-Paz, E. (2001). Genetic Algorithms. Encyclopedia of Computers and Computer History. Chicago, IL: Fitzroy Dearborn.
• Cantú-Paz, E. (1999). Implementing fast and flexible parallel genetic algorithms. In Chalmers, L. (Ed.), Practical Handbook of Genetic Algorithms. Volume III. pp. 65--84. Boca Raton, FL: CRC Press.

Other Publications

• Cantú-Paz, E., Cheung, S.-C., and Kamath, C. (2004), Retrieval of Similar Objects in Simulation Data Using Machine Learning Techniques, Proceedings of SPIE. Vol. 5298. Image Processing: Algorithms and Systems III. E. R. Dougherty, J. T. Astola, K. O. Egiazarian, Eds. (pp. 251--258). San Jose, CA.
• Kamath, C. and Cantú-Paz, E. (2002). Classification of bent-double galaxies: experiences with ensembles of decision trees. In Proceedings of the Fifth International Workshop on Mining Scientific Datasets. (held in conjunction with the Second SIAM International Conference on Data Mining.)
• Kamath, C., Cantú-Paz, E., Fodor, I.K. and N. Tang (2002). Classifying bent-double galaxies. IEEE Computing in Science and Engineering. 4(4), 52--60.
• Cantú-Paz, E. (2001). Supervised and unsupervised discretization methods for evolutionary algorithms. In Workshop on Optimization by Building and Using Probabilistic Models at GECCO 2001. (pp. 213--216). San Francisco, CA.
• Kamath, C. and Cantú-Paz, E. (2001). Creating ensembles of decision trees by randomizing the decision at a node. Signal and Imaging Sciences Workshop, Lawrence Livermore National Laboratory, November 19--20, 2001.
• Kamath, C., Cantú-Paz, E., Fodor, I.K., Tang, N. (2001). Using data mining techniques to find bent-double radio galaxies in the FIRST survey. Proceedings of SPIE. Vol. 4477. Astronomical Data Analysis, J.-L. Starck, F.D. Murtagh, Eds. (pp. 11--19). San Diego, CA.
• Cantú-Paz, E. (2000). On the effects of migration on the fitness distribution of parallel evolutionary algorithms. In Workshop on Evolutionary Computation and Parallel Processing at GECCO-2000. (pp. 3--6). Las Vegas, NV.
• Kamath, C., Cantú-Paz, E. (2000). On the design of a parallel object-oriented data mining toolkit. In Workshop on Distributed and Parallel Knowledge Discovery at KDD-2000. Boston, MA.
• Fodor, I., Cantú-Paz, E., Kamath, C., and Tang, N. (2000). Finding Bent-Double Radio Galaxies: A Case Study in Data Mining. Interface: Computer Science and Statistics. Volume 33. New Orleans, LA.
• Cantú-Paz, E. (1999). Migration policies, selection pressure, and parallel evolutionary algorithms. In Brave, S., Wu, A. (Eds.) Late Breaking Papers at the Genetic and Evolutionary Computation Conference. Orlando, FL.
• Kamath, C., E. Cantú-Paz, Tang, N. (1999). Sapphire: A High-Performance Object-Oriented Framework for Mining Scientific Datasets. In Third International Symposium for Computing in Object-Oriented Parallel Environments. San Francisco, CA.
• Cantú-Paz, E. and Mejía Olvera, M. (1995). Algoritmos genéticos paralelos. Soluciones Avanzadas, 3(17), 12--19.