2020 International Workshop on
Knowledge Transfer in Evolutionary Optimization
19 December, 2020, Chongqing, China

Aims and Scope

Evolutionary Algorithms (EAs) are nature-inspired population-based search methods which work on Darwinian principles of natural selection. Due to their strong search capability and simplicity of implementation, EAs have been successfully applied to solve many complex optimization problems, which cannot be easily solved by traditional mathematical programming approaches, such as linear programming, quadratic programming, and convex optimization.

Despite the great success enjoyed by EAs, it is worth noting that existing EA solvers usually conduct the search process from scratch, regardless how similar the new problem encountered is to those already solved in the past. Therefore, conventional EAs do not learn from previous problems and the search capabilities of the EA solvers do not automatically grow with problem-solving experiences. However, in reality, problems seldom exist in isolation, solving one problem may thus yield useful information for solving other related problems. In the literature, there is a growing interest in conducting research on evolutionary transfer optimization (ETO) in recent years: a paradigm that integrates EA solvers with knowledge learning and transfer across related domains to achieve better optimization efficiency and performance.

Being one of the emerging research areas in computational intelligence, there are many challenges and open research questions in ETO. This workshop aims at introducing and discussing the elementary concepts, the state-of-the-art techniques, as well as the future work directions of ETO.

Through this workshop, we will meet multiple targets which include:
    (1) promoting the research on ETO frameworks and algorithms towards more efficient and effective evolutionary optimization,
    (2) serving existing CIS members,
    (3) promoting CIS and attracting new members,
    (4) attracting industry participants.

Venue and Dates

  • Address: Chongqing, China
  • Dates: 19 December, 2020
  • Time: 2020/12/19 09:00-17:00
  • Tencent Meeting ID: 431 212 508

Invited Speakers (list in alphabetical order)

  • Prof. Yaochu Jin, IEEE Fellow, University of Surrey
        Talk:    Data-Driven Evolutionary Computation
  • Prof. Yew-Soon Ong, IEEE Fellow, Nanyang Technological University
        Talk:    Evolutionary Multitask Optimization
  • Prof. Xin Yao, IEEE Fellow, Southern University of Science and Technology
        Talk:    Many Facets of Learning to Optimise
  • Prof. Mengjie Zhang, IEEE Fellow, Victoria University of Wellington
        Talk:    Evolutionary Transfer Learning for Image Analysis and Pattern Recognition
  • Prof. Qingfu Zhang, IEEE Fellow, City University of Hong Kong
        Talk:    Use of problem relationship in design of heuristics

    • Program

    Program Schedule
    09:00 – 09:10
    Welcome Address
    Prof. Xiaofeng Liao, Chongqing University
    09:10 – 09:30
    Workshop Opening
    Prof. Kay Chen Tan, Hong Kong Polytechnic University
    09:30 – 10:15
    Keynote 1: Many Facets of Learning to Optimise
    Prof. Xin Yao, Southern University of Science and Technology
    10:15 – 11:00
    Keynote 2: Evolutionary Multitask Optimization
    Prof. Yew-Soon Ong, Nanyang Technological University
    11:00 – 11:45
    Keynote 3: Evolutionary Transfer Learning for Image Analysis and Pattern Recognition
    Prof. Mengjie Zhang, Victoria University of Wellington
    11:45 – 12:15
    Tutorial 1: Explicit Evolutionary Multitasking for Combinatorial Optimization: A Case Study on Capacitated Vehicle Routing Problem
    Prof. Liang Feng, Chongqing University
    12:15 – 14:00
    Lunch Break
    14:00 – 14:45
    Keynote 4: Use of problem relationship in design of heuristics
    Prof. Qingfu Zhang, City University of Hong Kong
    14:45 – 15:15
    Tutorial 2: Dynamic Evolutionary Multiobjective Optimization: A Knowledge Transfer Approach
    Prof. Min Jiang, Xiamen University
    15:15 – 15:45
    Tutorial 3: Evolutionary Computation for Automated Design of Deep Neural Networks
    Prof. Bing Xue, Victoria University of Wellington
    15:45 – 16:15
    Tutorial 4: An Introduction to Surrogate Models in Evolutionary Computation
    Prof. Handing Wang, Xidian University, China
    16:15 – 17:00
    Keynote 5: Data-Driven Evolutionary Computation
    Prof. Yaochu Jin, University of Surrey

    Organizing Committee

    Advisory Chairs:
  • Prof. Xiaofeng Liao, Chongqing University, China
  • Prof. Kay Chen Tan, Hong Kong Polytechnic University
    • Program Co-Chairs:
  • Prof. Min Jiang, Xiamen University, China
  • Prof. Handing Wang, Xidian University, China
  • Prof. Bing Xue, Victoria University of Wellington, New Zealand
  • Prof. Liang Feng, Chongqing University, China
    • Sponsors:
  • Chongqing University
    • Supported by IEEE Computational Intelligence Society, Xiamen Chapter.