Metaheuristic Optimization, Machine Learning and AI – Virtual Workshop

Dates:  March 8-12, 2021
Times:  Various

Deadline for Registrations was February 26, 2021
If you want to participate in this workshop and missed the online registration, send an email to

Simulated Annealing and Genetic Algorithms are widely known in statistics, but there are more modern general-purpose optimization algorithms, such as swarm-based algorithms and evolutionary algorithms, that have received scant attention in statistical research.  Examples of swarm-based algorithms are   Particle swarm, Bat, Cuckoo, Competitive Swarm, and examples of evolutionary algorithms are Differential Evolution and Imperialistic Competitive algorithm.  Recent work has increasingly shown this powerful class of nature-inspired meta-heuristic algorithms can flexibly tackle high-dimensional complex optimization problems in multiple disciplines, when they are used singly or when they are hybridized with other types of algorithms. Interestingly, these algorithms do not seem to require technical assumptions for them to work well and many also do not have proof of convergence. This workshop aims to promote discussions on recent advances in metaheuristics and their abundant use in machine learning and diverse applications in AI.

Confirmed Speakers:

Alan R. Vazquez, Department of Statistics, University of California Los Angeles USA
Christian Blum, Artificial Intelligence Research Institute Spain
Ray-Bing Chen, Department of Statistics, National Cheng Kung University Taiwan
Carlos A. Coello Coello, Department of Computer Science, Cinvestav-IPN Mexico
Abhishek Gupta, Singapore Institute of Manufacturing Technology, A*Star Singapore
Yaochu Jin, Department of Computer Science, University of Surrey UK
Seongho Kim, Department of Oncology, School of Medicine and Biostatistics Core, Karmanos Cancer Institute USA
Jinglai Li, School of Mathematics, University of Birmingham UK
Dietmar Maringer, Department of Business and Economics, University of Basel Switzerland
Soumya D. Mohanty, Department of Physics and Astronomy, U of Texas RGV USA
Jonas Mueller, Amazon Web Services USA
Frederick Phoa, Institute of Statistical Science, Academia Sinica Taiwan
Christina Ramirez, Department of Biostatistics, University of California Los Angeles USA
Christine Shoemaker, Department of Industrial Systems Engineering and Management, National University of Singapore
Noah Simon, Department of Biostatistics, University of Washington USA
Ponnuthurai Suganthan, School of Electrical & Electronic Engineering, Nanyang Technological University Singapore
Xin Tong, Department of Mathematics, National University of Singapore
Stefan Wager, Department of Graduate School of Business and Department of Statistics, Stanford University USA
Xin-She Yang, Department of Design Engineering and Mathematics, Middlesex University London UK
Gang George Yin, Department of Mathematics, University of Connecticut USA
Qingfu Zhang, Department of Computer Science, City University of Hong Kong

Postdoc/Graduate Student Speakers:

Marzie Rasekh, Department of Bioinformatics, Boston University USA
Mitchell Schepps, Department of Biostatistics, UCLA USA
Ye Tian, Department of Computing, Hong Kong Polytechnic University, Hong Kong and Institutes of Physical Science and Information Technology, Anhui University China

Tentative Schedule and Supporting Media

Printable Tentative Schedule
Titles and Abstracts
Registered Participants

  • Monday, March 8th  (Virtual – All times Eastern Standard Time (NY))
    Time Description Speaker
    7:45 Welcome  Weng Kee Wong, UCLA
    8-9:00 Nature-Inspired  Algorithms for Optimization: Challenges and Open Problems Xin-She Yang, Middlesex University London
    (Moderator: Weng Kee Wong, UCLA)
    9-10:00 An Overview of Evolutionary Multi-Objective Optimization Carlos Coello Coello, CINVESTAV-IPN
    (Moderator: Weng Kee Wong, UCLA)
    11-Noon Particle Swarm Optimization in Statistical Regression: Adaptive spline fitting and other case studies Soumya Mohanty, University of Texas, Rio Grande Valley
    (Moderator: Weng Kee Wong, UCLA)
    1:50-2:00 Remarks David Banks, SAMSI & Duke University
    2-3:00 Hybrid Metaheuristics Christian Blum, Spanish National Research Council
    (Moderator: Zhe Fei, UCLA)
    3:00 Adjourn

    Tuesday, March 9th  (Virtual – All times Eastern Standard Time (NY)

    Time Description Speaker
    8-9:00 A Two-Step Approach to the Search of Minimum Energy Designs via Swarm Intelligence Frederick Kin Hing Phoa, Academia Sinica
    (Moderator: Xin Tong, National University of Singapore)
    10-11:00 Global Optimization of Homogeneously Noisy, Expensive Multimodal Functions with RBF Surrogates Christine Shoemaker, National University of Singapore
    (Moderator: Xin Tong, National University of Singapore)
    Noon-1:00 Meta-Heuristics in Finance Dietmar Maringer, University of Basel
    (Moderator: Weng Kee Wong, UCLA)
    2-3:00 Entropy Estimation via Normalizing Flow Jinglai Li, University of Birmingham
    (Moderator: Weng Kee Wong, UCLA)
    4-5:00 Optimizing Designs for Adaptive Enrichment Clinical Trials Noah Simon, University of Washington
    (Moderator: Zhe Fei, UCLA)
    5:00 Adjourn

    Wednesday, March 10th  (Virtual – All times Eastern Standard Time (NY)

    Time Description Speaker
    9-10:00 Exploration Enhancement and Parameter Tuning for Nature-Inspired Metaheuristic Optimization Algorithms Xin Tong, National University of Singapore
    (Moderator: Kwok Pui Choi, National University of Singapore)
    10-11:00 Using Machine Learning in Stochastic Control and Filtering George Yin, University of Connecticut
    (Moderator: Xin Tong, National University of Singapore)
    Noon-1:00 Constructing Optimal Screening Designs for Effective Experimentation using Metaheuristics Alan R. Vazquez, UCLA
    (Moderator: Weng Kee Wong, UCLA)
    5-6:00 Machine Learning for Causal Inference Stefan Wager, Stanford University
    (Moderator: Tom Belin, UCLA)
    7-8:00 Transfer and Multi-task Evolutionary Computation Abhishek Gupta, Agency for Science, Technology and Research (A*STAR)
    (Moderator: Noah Simon, University of Washington)
    8:00 Adjourn

    Thursday, March 11th  (Virtual – All times Eastern Standard Time (NY)

    Time Description Speaker
    8-9:00 Mining Information from Biological Networks via Metaheuristics Ye Tian, Hong Kong Polytechnic University
    (Moderator: Kwok Pui Choi, National University of Singapore)
    9-10:00 Randomization Based Deep and Shallow Learning Algorithms Ponnuthurai Suganthan, Nanyang Technological University
    (Moderator: Weng Kee Wong, UCLA)
    Noon-1:00 Identifying Optimal Subgroups in Clinical Trials using Optimization Algorithms Marzie Rasekh, Boston University
    (Moderator: Mitchell Schepps, UCLA)
    3-4:00 Multi-Objective Optimization on Phase II Single-Arm Trial Designs Seongho Kim, Wayne State University/Karmanos Cancer Inst
    (Moderator: Alan R. Vazquez, UCLA)
    7-8:00 Introduction to Decomposition Based Multiobjective Evolutionary Algorithms (MOEA/D) Qingfu Zhang, City University of Hong Kong
    (Moderator: Alan R. Vazquez, UCLA)
    8:00 Adjourn

    Friday, March 12th  (Virtual – All times Eastern Standard Time (NY)

    Time Description Speaker
    8-9:00 Particle Swarm Stepwise (PaSS) Algorithm for Information Criterion Based Variable Selections Ray-Bing Chen, National Cheng Kung University
    (Moderator: Kwok Pui Choi, National University of Singapore)
    10-11:00 Data-driven Bayesian Evolutionary Optimization Yaochu Jin, University of Surrey
    (Moderator: Weng Kee Wong, UCLA)
    Noon-1:00 SARS-CoV-2 Worldwide Replication Drives Rapid Rise of Mutations across the Viral Genome and their Selection Christina Ramirez, UCLA
    (Moderator: Weng Kee Wong, UCLA)
    2-3:00 Optimizing Patient Enrollment in Clinical Trials by Metaheuristics Mitchell Schepps, UCLA
    (Moderator: Marzie Rasekh, Boston University)
    4-5:00 Metaheuristics in Automated Machine Learning with AutoGluon Jonas Mueller, Amazon Web Services
    (Moderator: Weng Kee Wong, UCLA)
    5:00 Adjourn