Dr. Rung-Tzuo Liaw 廖容佐
Assistant Professor
Computational Intelligence Lab
Department of Computer Science and Information Engineering
Fu
Jen Catholic University, Taiwan
Office: Room 628, Divine Word Academic Highrise
Email: rtliaw_at_csie.fju.edu.tw
Phone: +886-(0)2-29053875
Education
u PhD, Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan. (2017/07)
u MS, Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan. (2012/06)
u BS, Department of Psychology, National Taiwan University, Taiwan. (2009/06)
Research Interests
u Evolutionary Computation
u Evolutionary Multitasking and Transfer Learning
u Evolutionary Machine Learning and Data Mining
u Data-driven Optimization and Surrogates
u Evolutionary Multi-objective Optimization
Honors and Awards
u Merit PhD Thesis Award, Taiwanese Association for Artificial Intelligence.
u CEC’2017 Competition on Multitask Optimization First Runner Up.
u Honorary Member of The Phi Tau Phi Scholastic Honor Society of the Republic of China 2014.
Teaching
u Computer Programming (C and C++)
u An Introduction to Computer Science
u Operating System
u An Introduction to Aritificial Intelligence
u An Introduction to Evolutionary Computation
u Bio-inspired Computing
u Metaheuristic Algorithms
Publications
Journal Papers
1. R.-T. Liaw and Y.-W. Wen. Ensemble Learning through Evolutionary Multitasking: A Formulation and Case Study. IEEE Transactions on Emerging Topics in Computational Intelligence, Early Access, 2024.
2. R.-T. Liaw. A cooperative coevolution framework for evolutionary learning and instance selection. Swarm and Evolutionary Computation, 62:100840, 2021.
3. R.-T. Liaw, and C.-K. Ting. Evolution of biocoenosis through symbiosis with fitness approximation for many-tasking optimization. Memetic Computing, 12(4): 399–417, 2020.
4. C.-K. Ting, R.-T. Liaw, T.-C. Wang, and T.-P. Hong. Mining fuzzy association rules using a memetic algorithm based on structure representation. Memetic Computing, 10(1):15–28, 2018.
5. F.-I. Chung, R.-T. Liaw, Z.-Y. Jhuang, S.-Y. Hung, and C.-K. Ting. A novel book recommendation system based on multi-level association mining. Journal of Library and Information Science Research, 13(1), 2018.
6. C.-K. Ting, T.-C. Wang, R.-T. Liaw, and T.-P. Hong. Genetic algorithm with a structure-based representation for genetic-fuzzy data mining. Soft Computing, 21(11):2871–2882, 2017.
7. C.-K. Ting, X.-L. Liao, Y.-H. Huang, and R.-T. Liaw. Multi-vehicle selective pickup and delivery using metaheuristic algorithms. Information Sciences, 406–407:146–169, 2017.
Conference Papers
1. M.-Y. Ying and R.-T. Liaw. A Language-free Evolutionary Framework for Text-to-image Generation. In Proceedings of IEEE Congress on Evolutionary Computation, pages 1–8, 2024.
2. R.-T. Liaw and Y.-T. Lo. Evolutionary Multitask Reinforcement Learning Using Symbiosis in Biocoenosis Optimization. In Proceedings of IEEE Congress on Evolutionary Computation, pages 1–8, 2023.
3. C.-Y. Hsieh and R.-T. Liaw. Summit-assisted Evolutionary Multitasking. In Proceedings of IEEE Congress on Evolutionary Computation, pages 1–8, 2022.
4. T.-C. Wang, and R.-T. Liaw. Multifactorial Genetic Fuzzy Data Mining for Building Membership Functions. In Proceedings of IEEE Congress on Evolutionary Computation, PP, 2020.
5. T.-C. Wang, C.-Y. Lin, R.-T. Liaw, and C.-K. Ting. Empirical analysis of island model on large scale global optimization. In Proceedings of IEEE Congress on Evolutionary Computation, pages 342–349, 2019.
6. R.-T. Liaw and C.-K. Ting. Evolutionary manytasking optimization based on symbiosis in biocoenosis. In Proceedings of The Thirty-Third AAAI Conference on Artificial Intelligence, 2019.
7. R.-T. Liaw and C.-K. Ting. Incorporating fitness inheritance and k-nearest neighbors for evolutionary dynamic optimization. In Proceedings of IEEE Congress on Evolutionary Computation, pages 1345–1352, 2018.
8. R.-T. Liaw and C.-K. Ting. Evolutionary many-tasking based on biocoenosis through symbiosis: A framework and benchmark problems. In Proceedings of IEEE Congress on Evolutionary Computation, pages 2266–2273, 2017.
9. R.-T. Liaw, Y.-W. Chang, and C.-K. Ting. Solving the selective pickup and delivery problem using max-min ant system. In Proceedings of International Conference on Swarm Intelligence, pages 293–300, 2017.
10. T.-C. Wang, R.-T. Liaw, and C.-K. Ting. MOEA/D using covariance matrix adaptation evolution strategy for complex multi-objective optimization problems. In Proceedings of IEEE Congress on Evolutionary Computation, pages 983–990, 2016.
11. R.-T. Liaw and C.-K. Ting. Enhancing covariance matrix adaptation evolution strategy through fitness inheritance. In Proceedings of IEEE Congress on Evolutionary Computation, pages 1956–1963, 2016.
12. C.-K. Ting, T.-C. Wang, and R.-T. Liaw. An efficient representation for genetic-fuzzy mining of association rules. In Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, volume 2, pages 599–612, 2015.
13. C.-J. Lin, R.-T. Liaw, C.-C. Liao, and C.-K. Ting. Considering reputation in the selection strategy of genetic programming. In Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, volume 2, pages 533–542, 2015.
14. R.-T. Liaw and C.-K. Ting. Effect of model complexity for estimation of distribution algorithm in nk landscapes. In Proceedings of IEEE Symposium on Foundations of Computational Intelligence, pages 76–83, 2013.
Professional Activities
Secretary-general, Taiwanese Association for Artificial Intelligence (TAAI) (2023-)
Editorial Board Member, Memetic Computing, Springer (2020-)
Chair, IEEE CIS ISATC Intelligent Network Systems Task Force (2021-2023)
Chair, Special Session on Evolutionary Computation in Intelligent Transportation Systems (CEC 2019)
Co-chair, Special Session on Intelligent Transportation and Logistics Networks (WCCI 2018)