Basic Information

Computer Science Division
Associate Professor
Web site


Courses - Undergraduate
Algorithm and Data Structure, Java programming, Software Engineering, C programming
Courses - Graduate


Computational Intelligence, Soft Computing
Educational Background, Biography
Sep., 2006, Software College, Northeastern University, China, Bachelor of Engineering.
Mar. 2009, Software College, Northeastern University, China, Master of Engineering.
2006-2011, Neusoft (China), Alpine electronics R&D Europe GmbH (Germany), Software Engineer.
Mar. 2014, Graduate School of Design, Kyushu University, Japan, Doctor of Engineering.
Apr. 2014, the University of Aizu, Assistant Professor.
Apr. 2016, the University of Aizu, Associate Professor.
Current Research Theme
[1] Fitness Landscape of Evolutionary Computation
[2] Interactive Evolutionary Computation
[3] Chaos and Chaotic Evolution
[4] Fusion of Game Theory and Evolutionary Computation
[5] Machine Learning
[6] Software Engineering
Key Topic
Computational Intelligence, Neural Network, Fuzzy System, Evolutionary Computation, Chaos, Machine Learning, Software Engineering
Affiliated Academic Society
IEEE, Japanese Society for Evolutionary Computation


Hiking, Tennis
School days' Dream
To become a professor at university
Current Dream
To become a man of value
Try not to become a man of success, but rather to become a man of value. (Albert Einstein)
Favorite Books
"Analects" (Lunyu) by Confucius
Messages for Students
Please do your best in the University of Aizu!
Publications other than one's areas of specialization

Main research

Establishing theoretical fundamental of algorithmic mechanism design for evolutionary computation

We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This research is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm.

View this research

Dissertation and Published Works

1.Yan Pei,Natural Computing,,Chaotic Evolution: Fusion of Chaotic Ergodicity and Evolutionary Iteration for Optimization,2014,2.Yan Pei and Qingfu Zhao and Yong Liu,The Scientific World Journal,,Kernel Method Based Human Model for Enhancing Interactive Evolutionary Optimization,2015,3.,Yan Pei,6th Evolutionary Computation Meeting,,,,,,Chaotic Evolution,2014,4.,Yan Pei and Hideyuki Takagi,the 7th Evolutionary Computing Meeting,,,,,,Local Information of Fitness Landscape Obtained by Paired Comparison-based Memetic Search for Interactive Differential Evolution,2014,5.,Noboru Murata and Ryuei Nishii and Hideyuki Takagi and Yan Pei,Japanese Society for Evolutionary Computation Symposium 2014,,,,,,Estimation Methods of the Convergence Point of Moving Vectors Between Generations,2014,6.,Yan Pei,Japanese Society for Evolutionary Computation Symposium 2014,,,,,,Establishing theoretical fundamental of algorithmic mechanism design for evolutionary computation,2014,7.,Yan Pei and Hideyuki Takagi and Qiangfu Zhao and Yong Liu,2014 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC2014),,,,,,A Comprehensive Analysis on Optimization Performance of Chaotic Evolution and Its Parameter Distribution,2014,8.Yan Pei,10.1155/2015/591954,International Journal of Machine Learning and Cybernetics,,Algorithmic Mechanism Design of Evolutionary Computation,2015,9.Yan Pei,,Transactions of the Japanese Society for Artificial Intelligence,,Study on Efficient Search in Evolutionary Computation,2015,10.Yan Pei, Shaoqiu Zheng, Ying Tan and Hideyuki Takagi,10.1007/s13042-015-0388-8,International Journal of Machine Learning and Cybernetics,,Effectiveness of Approximation Strategy in Surrogate-assisted Fireworks Algorithm,2015,11.Yan Pei,10.1155/2015/704587,The Scientific World Journal,,From Determinism and Probability To Chaos: Chaotic Evolution Towards Philosophy and Methodology Of Chaotic Optimization,2015,12.,Yan Pei and Hideyuki Takagi,2015 IEEE Congress on Evolutionary Computation (IEEE CEC2015),,,,,,,Local Information of Fitness Landscape Obtained by Paired Comparison-Based Memetic Search for Interactive Differential Evolution,2015,13.,Yan Pei,2015 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC2015),,,Oct.,,,,Strategy Equilibrium of Evolutionary Computation: towards Its Algorithmic Mechanism Design,2015,14.,Yan Pei,2015 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC2015),,,,,,,Linear Principal Component Discriminate Analysis,2015,15.Yan Pei,10.1007/s11227-016-1829-1,The Journal of Supercomputing,,Principal Component Selection Using Interactive Evolutionary Computation,2016,16.,Yan Pei,The society of instrucment and control engineers, Tohoku chapter 305th meeting,,,,,,,Autoencoder Using Kernel Method,2016,17.,Yan Pei,Joint 8th International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems (SCIS \& ISIS2016),10.1109/SCIS-ISIS.2016.0040,,,,,,Data Compression with Linear Discriminant Analysis,2016,18.,Yan Pei,Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on,10.1109/SMC.2016.7844258,,,,,,Principal component selection of machine learning algorithms based on orthogonal transformation by using interactive evolutionary computation,2016