Basic Information

Affiliation
Computer Science Division
Title
Associate Professor
E-Mail
peiyan@u-aizu.ac.jp
Web site
http://www.u-aizu.ac.jp/~peiyan/

Education

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

Research

Specialization
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), Software Engineer and Project Manager.
2008-2010, 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<br>[2] Interactive Evolutionary Computation<br>[3] Chaos and Chaotic Evolution<br>[4] Fusion of Game Theory and Evolutionary Computation<br>[5] Machine Learning<br>[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

Others

Hobbies
Hiking, Tennis
School days' Dream
To become a professor at university
Current Dream
To become a man of value
Motto
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,10.1155/2015/591954,International Journal of Machine Learning and Cybernetics,,Algorithmic Mechanism Design of Evolutionary Computation,2015,4.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,2015,5.,Yan Pei,2015 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC2015),,,Oct.,,,,Strategy Equilibrium of Evolutionary Computation: towards Its Algorithmic Mechanism
Design,2015,6.Yan Pei,10.1007/s11227-016-1829-1,The Journal of Supercomputing,,Principal Component Selection Using Interactive Evolutionary Computation,2016,[1]. Yan Pei, "Chaotic Evolution: Fusion of Chaotic Ergodicity and Evolutionary Iteration for Optimization", Natural Computing, Springer, Vol.13 (1), pp.79-96, (2014).
[2]. Yan Pei and Hideyuki Takagi, "Accelerating IEC and EC searches with elite obtained by dimensionality reduction in regression spaces", Evolutionary Intelligence, Springer, Vol.6 (1), pp.27-40, (2013).
[3]. Yan Pei and Hideyuki Takagi, "Triple and Quadruple Comparison-Based Interactive Differential Evolution and Differential Evolution", Transaction of the Japanese Society for Evolutionary Computation, Vol.3 (2), pp.98-108 (2012) (in Japanese).
[4]. Yan Pei and Hideyuki Takagi, "Fourier Analysis of the Fitness Landscape for Evolutionary Search Acceleration", 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012), pp.2934-2940, Brisbane, Australia (June 10-15, 2012).