Time complexity analysis, which qualitatively describes the runtime of algorithms, is an important and fundamental problem in the field of evolutionary computing. Current theoretical results are mainly based on the simplified algorithms, while there are few analysis results of the existing state-of-the-art evolutionary algorithms in practical application. This tutorial focuses on introducing the estimation of the time complexity of evolutionary algorithms in practical application and aims at bridging the gap between the theoretical basis and practical application.
Outline of the tutorial:
- An introduction to the time complexity of evolutionary algorithms
- Research on the time complexity of evolutionary algorithm
- The estimation of time complexity of evolutionary algorithm based on an average gain model
- Experimental methods and procedures of estimating the time complexity of evolutionary algorithm
- A software system for estimating the time complexity of evolutionary algorithm
Learning outcomes:
By the conclusion of this tutorial, the learners will be able to:
- Understand the importance and challenges of estimating the time complexity of evolutionary algorithm;
- Develop a critical understanding of state-of-the art research on time complexity of evolutionary algorithm;
- Master the experimental methods and procedures of estimating the time complexity of evolutionary algorithm;
- Develop an understanding of the main functions and usage of the time complexity estimation system;
- Cultivate their academic interest which would be beneficial for their future research on the time complexity of evolutionary algorithm.
Expected length of the tutorial: 2h-4h
The level of the tutorial: Introductory or advanced
Presenter:
Han HUANG
School of Software Engineering, South China University of Technology, Guangzhou 510006, China
Websites: https://tinyurl.com/y68bc8wb
Biography: Han Huang received the B.Man. degree in information management and information system from the School of Mathematics, South China University of Technology (SCUT), Guangzhou, China, in 2003, and the Ph.D. degree in computer science from the South China University of Technology, Guangzhou, in 2008. He is currently a Professor with the School of Software Engineering, SCUT. His research interests include evolutionary computation, and swarm intelligence and their application. He is the Associate Editor of IEEE Transactions on Evolutionary Computation, and the Member of IEEE CS, SMC, ACM and CCF. He is also the reviewer for the peer-refereed journals including IEEE Transaction on Evolutionary Computation, IEEE Transaction on Cybernetics, Information Science, and Soft Computing. He has published over 60 papers in international journals such as IEEE TETC, IEEE TSE, IEEE TEVC, IEEE TIP, IEEE TFS, IEEE TII, IEEE CIM, and IEEE TCYB. Besides, he is the principal investigator of the research project“Research on computational time complexity comparison and estimation methods of evolutionary algorithms” funded by the National Natural Science Foundation of China (61876207).