于天立 Yu, Tian-Li
- Associate Professor, Department of Electrical Engineering, National Taiwan University
- Ph.D. University of Illinois at Urbana-Champaign, 2006
- M.S. University of Illinois at Urbana-Champaign, 2003
- B.S. National Taiwan University, 1997
- Office : MD 619
- TEL : +866-2-33669649
- Email :
- Office Hour : by email appointment
- Website : http://cc.ee.ntu.edu.tw/~tianliyu
Major Research Areas
Research Summary
I am interested in the theory and application of evolutionary computation (EC) and genetic algorithms (GAs). In particular, I am interested in understanding and the design competent GAs. My personal research centers at model building, linkage learning, and facet-wise modeling of GAs. More generally, I am also interested in artificial intelligence and machine learning.
Tian-Li Yu was born in Taipei, Taiwan on June 12, 1975. He graduated from the National
Taiwan University in Taipei, Taiwan with a bachelor degree in Electrical Engineering in
1997. He arrived the University of Illinois at Urbana-Champaign to pursue graduate study
in Computer Science in 2000 and became a member in the Illinois Genetic Algorithms
Laboratory in 2001. He received his master and Ph. D. degree from the University of Illinois at Urbana-Champaign in Computer Science in 2003 and 2006, respectively. Starting from 2007, Yu engaged in academic work as an assistant professor in the National Taiwan University.
Journal articles & book chapters
1. Fan, K.-C., Yu T.-L. Yu, & Lee, J.-T., “Linkage learning by number of function evaluations estimation: Practical view of building blocks” , Information Science , Vol. 230 , 162-182-, 2013
2. Yu, T.-L., Goldberg, D. E., Sastry, K., Lima, C., & Pelikan, M, “Dependency Structure Matrix, Genetic Algorithms, and Effective Recombination” , Evolutionary Computation , Vol. Vol. 17, No. 4 , 595-626-, Dec. 2009
3. Li, M., Goldberg, D. E., Sastry, K. & Yu, T.-L., “Real-Coded ECGA for Solving Decomposable Real-Valued Optimization Problems” , Linkage in Evolutionary Computation , 61-86-, May 2008
4. Yu, T.-L., Yassine, A. A., & Goldberg, D. E., “An Information Theoretic Method for Developing Modular Architectures Using Genetic Algorithms” , Research in Engineering Design , Vol. 18 , 91-109-, Aug. 2007
5. Yu, T.-L., Sastry, K., & Goldberg, D. E., “Population Sizing to Go: Online Adaptation Using Noise and Substructural Measurements” , in Parameter Setting in Evolutionary Algorithms, Editors: Lobo, F. G., Lima, C. F., & Michalwicz, Z. , 205-224-, Jan. 2007
6. Yu, T.-L., Santarelli, S., & Goldberg, D. E., “Military Antenna Design Using a Simple Genetic Algorithm and hBOA” , in Scalable Optimization via Probabilistic Modeling from Algorithms to Applications, Chapter 12. Editors: Pelikan, M., Sastry, K , Jan. 2006
7. Yu, T.-L., Yassine, A., & Goldberg, D. E., “Double Duty: Genetic Algorithms for Organizational Design and Genetic Algorithms Inspired by Organizational Theory” , in Handbook of Nature Inspired Computing for Economy and Management, Chapter 28. Editors: Rennard, J.-P. , Jan. 2006
8. Santarelli, S., Yu, T.-L., Goldberg, D. E., Altshuler, E., O’Donnell, T., Southall, H., & Mailloux, R., “Military Antenna Design Using Simple and Competent Genetic Algorithms” , Mathematical and Computer Modelling , Vol. 43 , 990-1022-, Jan. 2006
Conference & proceeding papers:
1. Chen, C.-S., Hsu, H.-W., & Yu, T.-L., “Fast algorithm for fair comparison of genetic algorithms” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018) , Kyoto, Japan , Jul. 2018
2. Lin, Y.-J., & Yu, T.-L., “Investigation of the exponential population scheme for genetic algorithms” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018) , Kyoto, Japan , Jul. 2018
3. Yu, J.-Y., Chen, I., & Yu, T.-L., “A diversity preservation scheme for DSMGA-II to conquer the hierarchical difficulty” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017) , Berlin, German , Jul. 2017
4. Chen, P.-L., Peng, C.-J., & Yu, T.-L., “Two-edge graphical linkage model for DSMGA-II” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017) , Berlin, German , Jul. 2017
5. Li, S.-C., & Yu, T.-L., “Speeding up DSMGA-II on CUDA platform” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017) , Berlin, German , Jul. 2017
6. Chang, C.-H. & Yu, T.-L., “Investigation on Parameterless Schemes for DSMGA-II” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2016 Companion) , Denver, Colorado, USA , Jul. 2016
7. Hsu, S.-H., & Yu, T.-L., “Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015) , Madrid, Spain , Jul. 2015
8. Tung, Y.-F., & Yu, T.-L., “Theoretical Perspective of Convergence Complexity of Evolutionary Algorithms Adopting Optimal Mixing” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015) , Madrid, Spain , Jul. 2015
9. Su, Y.-E. & Yu, T.-L., “Use model building on discretization algorithms for discrete EDAs To work on real-valued problems” , IEEE Congress on Evolutionary Computation (CEC 2014) , Beijing, China , Jul. 2014
10. Wang, S.-M., Tung, Y.-F., & Yu, T.-L., “Investigation on efficiency of optimal mixing on various linkage sets” , IEEE Congress on Evolutionary Computation (CEC 2014) , Beijing, China , Jul. 2014
11. Tung, H.-Y., Ma, W.-C., & Yu, T.-L., “Novel traffic signal timing adjustment strategy based on Genetic Algorithm” , IEEE Congress on Evolutionary Computation (CEC 2014) , Beijing, China , Jul. 2014
12. Chou, C.-Y., & Yu, T.-L., “Using representative strategies for finding Nash equilibria” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2013) , Amsterdam, Dutch , Jul. 2013
13. Hsu, P.-C., & Yu, T.-L., “A niching scheme for EDAs to reduce spurious dependencies” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2013) , Amsterdam, Dutch , Jul. 2013
14. Wang, S.-M., Wu, J.-W., Chen, W.-M., & Yu, T.-L., “Design of test problems for discrete estimation of distribution algorithms” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2013) , Amsterdam, Dutch , Jul. 2013
15. Shao, C.-Y., & Yu, T.-L., “Speeding up model building for ECGA on CUDA platform” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2013) , Amsterdam, Dutch , Jul. 2013
16. Chen, W.-M., Hsu, C.-Y., Yu, T.-L., & Chien, W.-C., “Effects of discrete hill climbing on model building for estimation of distribution algorithms” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2013) , Amsterdam, Dutch , Jul. 2013
17. Chen, W.-M., Shao, C.-Y., Hsu, P.-C., Yu, T.-L., “A test problem with adjustable degrees of overlap and conflict among subproblems” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2012) , Philadelphia, USA , Jul. 2012
18. Lee, H., Yu, T.-L., “Off-line building block identification: detecting building blocks directly from fitness without genetic algorithms” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2012) , Philadelphia, USA , Jul. 2012
19. Ho, T.-Y., Yu, T.-L., “A linkage-learning niching in estimation of distribution algorithm” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2012) , Philadelphia, USA , Jul. 2012
20. Hsueh, C.-H., Yu, T.-L., “Affective content based music video paring system using real coded GA” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2012) , Philadelphia, USA , Jul. 2012
21. Lee, J.-T., Fan, K.-C., & Yu, T.-L., “The essence of real-valued characteristic function for pairwise relation in linkage learning for EDAs” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2011) , Dublin, Ireland , Jul. 2011
22. Chang, J.-H., Hsueh, C.-H., Lee, H., Yu, T.-L., Ho, T.-Y., “A test function with full controllability over overlapping: estimation of distribution algorithms” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2011) , Dublin, Ireland , Jul. 2011
23. Fan, K.-C., Yu, T.-L., Lee, J.-T., “Interaction detection by NFE estimation: a practical view of building blocks” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2011) , Dublin, Ireland , Jul. 2011
24. Fan, K.-C., Lee, J.-T., Yu, T.-L., & Ho, T.-Y., “Interaction-detection metric with differential mutual complement for dependency structure matrix genetic algorithm” , IEEE Congress on Evolutionary Computation , Barcelona, Spain , Jul. 2010
25. Lien, T.-C., Yu, T.-L. & You, Y.-S., “Co-evolution of cooperative strategies under egoism” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2010) , Portland, Oregon , Jul. 2010
26. You, Y.-S., Yu, T.-L., & Lien, T.-C., “Psychological preference-based optimization framework on the nurse scheduling problem” , Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2010) , Porland, Oregon , Jul. 2010
27. Lin, W.-K. & Yu, T.-L., “Co-evolvability of games in coevolutionary genetic algorithms” , Proceedings of the Genetic and Evolutionary Computation Conference 2009 (GECCO 2009) , Montreal, Canada , Jul. 2009
28. Chen, S.-C. & Yu, T.-L., “Difficulty of linkage learning in estimation of distribution algorithms” , Proceedings of the Genetic and Evolutionary Computation Conference 2009 (GECCO 2009) , Montreal, Canada , Jul. 2009
29. Yu, T.-L. & Lin, W.-K., “Optimal sampling of genetic algorithms on polynomial regression” , Proceedings of the Genetic and Evolutionary Computation Conference 2008 (GECCO 2008) , Altlanta, USA , Jul. 2008
30. Li, M., Goldberg, D. E., Sastry, K., & Yu, T.-L., “Real-coded ECGA for solving decomposable real-valued optimization problems” , IEEE Congress on Evolutionary Computation , Singapore , Sep. 2007
31. Li, M., Goldberg, D. E., Sastry, K., & Yu, T.-L., “Performance analyses of factorization based on gaussian PDF in rECGA” , Third International Conference on Natural Computation (ICNC 2007) , Haikou, China , Aug. 2007
32. Llorà, X., Sastry, K., Yu, T.-L., & Goldberg, D. E., “Do not match, inherit: Fitness surrogates for genetics-based machine learning techniques” , Proceedings of the Genetic and Evolutionary Computation Conference 2007 , Seattle , Jul. 2007
33. Yu, T.-L., Sastry, K., Goldberg, D. E., & Pelikan, M., “Population sizing for entropy-based model building in discrete estimation of distribution algorithms” , Proceedings of the Genetic and Evolutionary Computation Conference 2007 , Seattle , Jul. 2007
34. Yu, T.-L., & Goldberg, D. E., “Conquering Hierarchical Difficulty by Explicit Chunking: Chromosome Compression” , Proceedings of the Genetic and Evolutionary Computation Conference 2006 , Seattle, Washington, USA , Jul. 2006
35. Yu, T.-L., Sastry, K., & Goldberg, D. E., “Online Population Size Adjusting Using Noise and Substructural Measurements” , 2005 IEEE Congress on Evolutionary Computation (CEC 2005) , Edinburgh, UK , Sep. 2005
36. Yassine, A., Goldberg, D. E., & Yu, T.-L., “Simple Models of Hierarchical Organizations” , Simple Models of Hierarchical Organizations. Academy of Management 2005 (AOM 2005) , Honolulu, Hawaii, USA , Aug. 2005
37. Yu, T.-L., Sastry, K., & Goldberg, D. E., “Linkage Learning, Overlapping Building Blocks, and Systematic Strategy for Scalable Recombination.” , Proceedings of the Genetic and Evolutionary Computation Conference 2005 (GECCO 2005) , Washington, D. C., USA , Jun. 2005
38. Goldberg, D. E, Yassine, A., & Yu, T.-L., “Calculating efficient team size: Balancing deciding and doing as an elementary optimization problem” , Proceedings of the ASME 2004 International Design Engineering Technical Conferences, 16th International Conference on Design The , Salt Lake City, Utah, USA , Sep. 2004
39. Yu, T.-L., & Goldberg, D. E., “Toward an understanding of the quality and efficiency of model building for genetic algorithms” , Proceedings of the Genetic and Evolutionary Computation Conference 2004 (GECCO 2004) , Seattle, Washington, USA , Jun. 2004
40. Yu, T.-L., & Goldberg, D. E., “Dependency structure matrix analysis: Off-line utility of the dependency structure matrix genetic algorithm” , Proceedings of the Genetic and Evolutionary Computation Conference 2004 (GECCO 2004) , Seattle, Washington, USA , Jun. 2004
41. Santarelli, S., Goldberg, D. E., & Yu, T.-L., “Optimization of a constrained feed network for an antenna array using simple and competent genetic algorithm techniques” , Genetic and Evolutionary Computation Conference 2004 (GECCO 2004), Military and Security Application of Evolutionary Computation , Seattle, Washington, USA , Jun. 2004
42. Yu, T.-L., Chen, Y.-p., Goldberg, D. E., & Chen, J.-H., “An adaptive sampling scheme for genetic algorithms on the sampled OneMax problem” , Proceedings of Artificial Neural Networks in Engineering 2003 (ANNIE 2003) , St. Louis, Missouri, USA , Nov. 2003
43. Yu, T.-L., Goldberg, D. E., Yassine, A. & Chen, Y.-p., “Genetic algorithm design inspired by organizational theory: Pilot study of a dependency structure matrix driven genetic algorithm” , Proceedings of Artificial Neural Networks in Engineering 2003 (ANNIE 2003) , St. Louis, Missouri, USA , Nov. 2003
44. Yu, T.-L., Yassine, A. & Goldberg, D. E., “A genetic algorithm for developing modular product architectures” , Proceedings of the ASME 2003 International Design Engineering Technical Conferences, 15th International Conference on Design The , Chicago, Illinois, USA , Sep. 2003
45. Yu, T.-L., Goldberg, D. E., & Sastry, K., “Optimal sampling and speed-up for genetic algorithms on the sampled OneMax problem” , Proceedings of the Genetic and Evolutionary Computation Conference 2003 (GECCO 2003) , Chicago, Illinois, USA , Jul. 2003
Books:
1. Le, L., G. L. Donohue, K. Hoffman, and C. H. Chen, “Optimum Airport Capacity Utilization under Congestion Management at NY LaGuardia Airport” ,
Patents:
1. Yu, T.-L., Goldberg, D. E., & Yassine, A., “Methods and Programs for Optimizing Problem Clustering” , US Patent No. 7,280,986., Oct. 2007
Other publication:
1. , “An Improved Simulation Budget Allocation Procedure to Efficiently Select the Optimal Subset of Many Alternatives” , Jan.