Multitask Optimization is a paradigm in the optimization literature that focuses on solving multiple self-contained tasks at the same time. Inspired by the well-established concepts of transfer learning and multi-task learning in predictive analytics, the key motivation behind multitask optimization is that if optimization tasks are related to each other (in terms of their optimal solutions, or the general characteristics of their function landscapes), then the search progress on one can be transferred to substantially speedup the search on the other. Notably, the success of the paradigm is not necessarily limited to one-way knowledge transfers from simpler to more complex tasks. In fact, in an attempt to intentionally solve a harder task, several simpler ones may often be unintentionally solved.
CEC 2019 Competition on Evolutionary Multi-task Optimization ,IEEE Congress on Evolutionary Computation, 10-13 JUNE 2019, WELLINGTON, NEW ZEALAND..
CEC 2018 Competition on Evolutionary Multi-task Optimization,IEEE World Congress on Computational Intelligence, 2018 July 8-13, Rio de Janeiro, Brazil
CEC 2017 Competition on Evolutionary Multi-task Optimization,IEEE Congress on Evolutionary Computation, 2017 June 5-8, Donostia - San Sebastian, Spain
Dr. Ong and Dr. Gupta gave a tutorial on "Evolutionary Multitasking and Implications for Cloud Computing" in Sendai, Japan. The slides can be downloaded here.
Dr. Ong gave a Keynote at Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2016), Canberra, Australia, 2-5 February 2016 "Multifactorial Optimization: Towards Evolutionary Multitasking" in Canberra, Australia. The slides can be downloaded here.
- Y. Chen, J. Zhong, L. Feng & J. Zhang. “An Adaptive Archive-Based Evolutionary Framework for Many-Task Optimization”. IEEE Transactions on Emerging Topics in Computational Intelligence, 2019.
- J. Yin, A. Zhu, Z. Zhu, Y. Yu & X. Ma, “Multifactorial Evolutionary Algorithm Enhanced with Cross-task Search Direction”. IEEE Congress on Evolutionary Computation (CEC), 2019.
- L. Zhou, L. Feng, K. Liu, C. Chen, S. Deng, T. Xiang & S. Jiang. “Towards Effective Mutation for Knowledge Transfer in Multifactorial Differential Evolution”. IEEE Congress on Evolutionary Computation (CEC), 2019.
- X. Zheng, Y. Lei, A. K. Qin, D. Zhou, J. Shi & M. Gong. “Differential Evolutionary Multi-task Optimization”. IEEE Congress on Evolutionary Computation (CEC), 2019.
- A. Gupta & Y. S. Ong. “Back to the roots: Multi-x evolutionary computation”. Cognitive Computation, 2019.
- A. Rauniyar, R. Nath & P. K. Muhuri. “Multi-factorial evolutionary algorithm based novel solution approach for multi-objective pollution-routing problem”. Computers & Industrial Engineering, 2019.
- Z. Liang, J. Zhang, L. Feng & Z. Zhu. “A hybrid of genetic transform and hyper-rectangle search strategies for evolutionary multi-tasking”. Expert Systems with Applications, 2019.
- H. T. T. Binh, N. Q. Tuan & D. C. T. Long. “A multi-objective multi-factorial evolutionary algorithm with reference-point-based approach”. IEEE Congress on Evolutionary Computation (CEC), 2019.
- Q. Shang, L. Zhang, L. Feng, Y. Hou, J. Zhong, A. Gupta & H. L. Liu. “A Preliminary Study of Adaptive Task Selection in Explicit Evolutionary Many-Tasking”. IEEE Congress on Evolutionary Computation (CEC), 2019.
- C. Jin, P. W. Tsai & A. K. Qin. “ A Study on Knowledge Reuse Strategies in Multitasking Differential Evolution”. IEEE Congress on Evolutionary Computation (CEC), 2019.
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