于天立副教授的著作列表 - Publication List of Tian-Li Yu

Publication List of 于天立 Tian-Li Yu

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, 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. 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, 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, 2007
  6. 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, 43, 990-1022, 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., 2006
  8. 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, 2006

Conference & proceeding papers:

  1. Chiang, T.-C., Chang, C.-H., & Yu, T.-L., “A Novel Symbolic Regressor Enhancer Using Genetic Programming,” IEEE Congress on Evolutionary Computation (IEEE CEC 2024), 1-8, Yokohama, Japan, Jul. 2024
  2. Hsu, T.-H., Chang, C.-H., & Yu, T.-L., “Program Synthesis on Single-Layer Loop Behavior in Pure Functional Programming,” IEEE Congress on Evolutionary Computation (IEEE CEC 2024), 1-8, Yokohama, Japan, Jul. 2024
  3. Liu, J.-C., Chang, C.-H., Sun, S.-H., & Yu, T.-L., “Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures,” International Conference on Learning Representation (ICLR 2024), Vienna, Austria, May 2024
  4. Chang, C.-H., Chiang, T.-C., Hsu, T.-H., Chuang, T.-S., Fang, W.-Z., & Yu, T.-L., “Taylor Polynomial Enhancer using Genetic Programming for Symbolic Regression,” Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2023 Companion), 543-546, Lisbon, Portugal, Jul. 2023
  5. Fang, W.-Z., Chang, C.-H., Liu, J.-C., & Yu, T.-L., “GP with Ranging-Binding Technique for Symbolic Regression,” Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2023 Companion), 563-566, Lisbon, Portugal, Jul. 2023
  6. Huang, B.-W., Fang, W.-Z., Liao, H.-C., & Yu, T.-L., “Relational Bayesian Optimization for Permutation,” Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2023 Companion), 471-474, Lisbon, Portugal, Jul. 2023
  7. Liao, H.-C., Fang, W.-Z., & Yu, T.-L., “Adaptive Donor Selection Mixing for Multi-objective Optimization: an Enhanced Variant of MO-GOMEA,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2023), 695-703, Lisbon, Portugal, Jul. 2023
  8. Ngai, C.-M., & Yu, T.-L., “Improving DSMGA-II Performance on Hierarchical Problems by Introducing Preservative Back Mixing,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2022), 455-458, Boston, USA, Jul. 2022
  9. Huang, L.-J., & Yu, T.-L., “TAGA: A Transfer-Based Black-Box Adversarial Attack with Genetic Algorithms,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2022), 712-720, Boston, USA, Jul. 2022
  10. Ohnishi, K., Koga, D., & Yu, T.-L., “Test Problem in Which Bits Used for Fitness Calculation Depend on Bit Pattern,” IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), 1-9, Orlando, USA, Dec. 2021
  11. Chen, J.-W., Lu, M.-C., & Yu T.-L., “Continuous Optimization by Hierarchical Gaussian Mixture with Clustering Embedded Resource Allocation,” Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2020 Companion), 191-192, Cancun, Mexico, Jul. 2020
  12. Ohnishi, K., Ikeda, S., & Yu, T.-L., “A Test Problem with Difficulty in Decomposing into Sub-problems for Model-based Genetic Algorithms,” Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO 2020 Companion), 221-222, Cancun, Mexico, Jul. 2020
  13. Yang, C.-H., Cheong, H.-T., & Yu, T.-L., “Comparison of GAs in Black-Box Scenarios,” Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2019 Companion), 310-311, Prague, Czech, Jul. 2019
  14. Liao, Y.-Y., Hsu, H.-W., Juang, Y.-L., & Yu, T.-L., “On the investigation of population sizing of genetic algorithms using optimal mixing,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2019), 820-828, Prague Czech Republic, Jul. 2019
  15. Ohnishi, K., Yoshikawa, T., & Yu T.-L., “An Intuitive and Traceable Human-based Evolutionary Computation System for Solving Problems in Human Organizations,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2019), 1457-1464, Prague, Czech, Jul. 2019
  16. 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), 913-920, Kyoto, Japan, Jul. 2018
  17. 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), 975-982, Kyoto, Japan, Jul. 2018
  18. 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), 841-848, Berlin, German, Jul. 2017
  19. 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), 745-752, Berlin, German, Jul. 2017
  20. Li, S.-C., & Yu, T.-L., “Speeding up DSMGA-II on CUDA platform,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017), 809-816, Berlin, German, Jul. 2017
  21. Chang, C.-H. & Yu, T.-L., “Investigation on Parameterless Schemes for DSMGA-II,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2016 Companion), 85-86, Denver, Colorado, USA, Jul. 2016
  22. 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), 519-526, Madrid, Spain, Jul. 2015
  23. 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), 535-542, Madrid, Spain, Jul. 2015
  24. 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), 2491-2498-2498, Beijing, China, Jul. 2014
  25. 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), 2475-2482-2482, Beijing, China, Jul. 2014
  26. 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), 2353-2360-2360, Beijing, China, Jul. 2014
  27. Chou, C.-Y., & Yu, T.-L., “Using representative strategies for finding Nash equilibria,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2013), to appear, Amsterdam, Dutch, Jul. 2013
  28. 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), 375-382, Amsterdam, Dutch, Jul. 2013
  29. 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), 407-414, Amsterdam, Dutch, Jul. 2013
  30. 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), 1197-1204, Amsterdam, Dutch, Jul. 2013
  31. 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), 367-374, Amsterdam, Dutch, Jul. 2013
  32. 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), 257-264, Philadelphia, USA, Jul. 2012
  33. 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), 641-648, Philadelphia, USA, Jul. 2012
  34. 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), 649-650, Philadelphia, USA, Jul. 2012
  35. 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), 1427-1428, Philadelphia, USA, Jul. 2012
  36. 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), 569-576, Dublin, Ireland, Jul. 2011
  37. 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), 61-62, Dublin, Ireland, Jul. 2011
  38. 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), 71-72, Dublin, Ireland, Jul. 2011
  39. 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, 1-8, Barcelona, Spain, Jul. 2010
  40. 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), 697-704, Portland, Oregon, Jul. 2010
  41. 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), 1319-1320, Porland, Oregon, Jul. 2010
  42. 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), 1869-1870, Montreal, Canada, Jul. 2009
  43. 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), 397-404, Montreal, Canada, Jul. 2009
  44. 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), 1089-1096, Altlanta, USA, Jul. 2008
  45. 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, 1435-1438, Singapore, Sept. 2007
  46. 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), IV, 362-369, Haikou, China, Aug. 2007
  47. 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, 1798–1805, Seattle, Jul. 2007
  48. 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, 601-608, Seattle, Jul. 2007
  49. Yu, T.-L., & Goldberg, D. E., “Conquering Hierarchical Difficulty by Explicit Chunking: Chromosome Compression,” Proceedings of the Genetic and Evolutionary Computation Conference 2006, 1385-1392, Seattle, Washington, USA, Jul. 2006
  50. 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), 3, 2491–2498, Edinburgh, UK, Sept. 2005
  51. 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
  52. 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), 1217-1224, Washington, D. C., USA, Jun. 2005
  53. 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, DTM-57402, Salt Lake City, Utah, USA, Sept. 2004
  54. 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
  55. 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), 355-366, Seattle, Washington, USA, Jun. 2004
  56. 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), 367-378, Seattle, Washington, USA, Jun. 2004
  57. 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), 39-44, St. Louis, Missouri, USA, Nov. 2003
  58. 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), 327–332, St. Louis, Missouri, USA, Nov. 2003
  59. 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, DTM-48657, Chicago, Illinois, USA, Sept. 2003
  60. 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), 1554-1565, Chicago, Illinois, USA, Jul. 2003

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