Laborarory of Intelligent Computation and Natural Computation
1. Research Work
The main research fields of the Intelligent Computation Research Office include intelligent computation, nature-inspired computation and applications.
Intelligent Computation is the new advance in AI, discovering heuristic information from computing process, acquiring intelligence, and finally obtaining knowledge. Evolutionary Computation, Neural Computing and Fuzzy Computation are three important research fields in Intelligent Computation. Further progresses include learning from and simulating natural and social systems by evolving rules and process, and designing algorithms and solving problems by borrowing ideas from theories and methods of natural science, at present, all of which are generalized as Natural Computation.
• Evolutionary Algorithm
• Evolutionary algorithm for continuous function optimization and combinatorial optimization
• Algorithms for optimization and multi-objective optimization in dynamic environment
• Theory of evolutionary algorithm, including convergence , convergence rate and computation complexity analysis
• Parallel model realization of evolutionary algorithm and related techniques
• Automatic Programming
• Complexity theory of automatic programming
• Evolutionary algorithm (EA) and program structure (PS)
• Methodology of automatic programming
• Artificial intelligence (AI) and computational intelligence (CI)
• Methods of automatic programming, including genetic programming based on syntax tree (reverse polish notation) (GP), gene expression programming (GEP), and chemical equation programming (CEP)
• Applications of automatic programming
• Evolutionary Software
• Foundation of Theoretical Basis of Adaptive and Reconfigurable Software Based on Evolutionary Computation
• Development of evolutionary software system, which is able to build certain programs according to the certain type of problems automatically
• Development of modeling software based on automatic programming, which is able to automatically build models or determine key parameters of models according to the raw data and the demands of users
• Development of representative and international advanced application software with independent copyright
• Dynamic Multi-object Optimization and decision-making
• Research on traits and formalized description of multi-object optimization problem
• Multi-object optimization evolutionary algorithms based on various evolutionary strategies and realizations
• Algorithm evaluation from the theory and the experiment angles
• Applications of research findings in scientific planning of major engineering works, military system an d command decision, and industrial and engineering design, etc., in the field of dynamic multi-object optimization
• Theory, methodology, realization technology and support environment for multi-object optimization problems
• Data Mining and KDD Automation
• Evolutionary algorithms for high-quality knowledge (complex ordinary differential equation or partial differential equation model, etc.) discovery from dynamic data of large complex systems, such as meteorological data, financial data and satellite data
• Evolutionary modeling: building models for some complex systems using automatic programming, such as function model, differential equation model and circuit model
• Real time modeling: modeling and prediction according to large-scale actual data, such as meteorological data, financial data and satellite data, using parallel evolutionary algorithm in distributed parallel environment
• Intelligent modeling and analysis software with independent copyright
• Evolvable Hardware (EHW)
• Evolvable hardware design, integrated with evolutionary computing and programmable device
• Software-hardware co-evolution system and Soc design based on evolvable hardware
• Evolvable hardware system based on hardware and software integration, automatic system design, and autonomic dynamic reconfiguration
• Adaptive design, self system diagnosis and self system repairing for EHW self-evolution
• Adaptive EHW system with independent copyright
• Evolutionary Cryptosystem
• Cryptography based on evolutionary computation
• Evolutionary methods for cryptogram design, evaluation and related tools
• Design of encrypt ASIC based on evolvable hardware technology
• Neural Computing and Fuzzy Computation
• Evolutionary neural networks and evolutionary support vector machines
• Theory of fuzzy set and rough set
• Theory and applications of fuzzy systems
• Artificial intelligence with uncertainty: representation, processing and understanding
• Fuzzy knowledge system
• Design and applications of fuzzy neural networks combining neural computing and fuzzy computation
• Intelligent Computation and Pattern Recognition
• Pattern recognition, data mining and knowledge discovery, based on theory and methods of intelligent computation
• Image processing and pattern recognition
• Pattern recognition system with independent copyright
• Theory System of Natural Computation
• Theory and methods of evolutionary computation combined with neural computing and fuzzy computation
• Methodology of problem solution, based on rules of natural system and social system and basic theories of natural science
• Basic theory system of natural computation, based on theory and methods of intelligent computation, and combined with theories of natural science
• Support Platform of Intelligent Computation and Natural Computation
• Support platform of intelligent computation and natural computation, software environment for algorithm design and parallel processing environment for large-scale complex problems solution, based on large-scale parallel computer and distributed parallel environment
• Support platform of evolvable hardware based on programmable device
• Hardware and software support for adaptive system
2. Faculty Members
Prof. Lishan Kang, Prof. Yuanxiang Li, Prof. Lixin Ding, Prof. Zhijian Wu, Prof. Hongqing Cao, Prof. Wenyong Dong, Dr. Lingling Wang
3. Achievements
3.1 Research Project
Table of Projects List
Project Title |
Period |
Support Fund |
Project No. |
Subsidize Amount |
Theory and Applications of Dynamic Evolutionary Algorithm. |
2005-2007 |
National Natural Science Foundation of China |
60473014 |
￥ 220,000 |
Researches on dynamic multi-object TSP |
2005-2007 |
National Natural Science Foundation of China |
60473081 |
￥ 200,000 |
The Evolvable Hardware Based SOC System Design |
2004-2006 |
National Natural Science Foundation of China |
60442001 |
￥ 200,000 |
A Kind of Dynamic Evolutionary Algorithm and Application |
2003-2005 |
The Research Fund for the Doctoral Program of Higher Education |
20030486049 |
￥ 60,000 |
Researches on Key Techniques of SOC Software-hardware Co-design |
2002-2004 |
National 863 Program |
2002AA1Z1490 |
￥ 700,000 |
Theory, Method and Application of evolutionary computation |
2002-2005 |
National Natural Science Foundation of China (Key) |
60133010 |
￥ 800,000 |
Computational Intelligence Techniques for Multi-object Optimization |
2003-2005 |
National Natural Science Foundation of China |
60204001 |
￥ 200,000 |
Multi-object Optimization and Its Application Based on Evolutionary Algorithm |
2002-2004 |
The Youth Chenguang Project of Science and Technology of Wuhan City |
20024001002 |
￥ 50,000 |
Evolutionary Modeling Software Development for Applications in various fields |
2001-2003 |
The Youth Chenguang Project of Science and Technology of Wuhan City |
20015005037 |
￥ 50,000 |
Theory and Applications of Automatic Programming |
2001-2003 |
National Natural Science Foundation of China |
60073043 |
￥ 160,000 |
Evolutionary Computation for Complex Science Research |
2001-2003 |
National Natural Science Foundation of China |
70071042 |
￥ 120,000 |
Theory and Application of Evolutionary Computation in Distributed Parallel Processing Environment |
2000-2002 |
Foundation for University Key Teacher by the Ministry of Education |
|
￥ 120,000 |
3.2 Publication
• Kang Lishan, Li Yan , Chen Yuping ， A Tentative Research on Complexity of Automatic Programming, Wuhan University Journal of Natural Sciences, Vol.6, No.1-2 (2001), 59-62.
• Hongqing Cao, Lishan Kang, Yuping Chen, Modeling of system of ordinary differential equations for dynamic system: an experimental study based on different search techniques, Journal of Computer Research & Development ， Vol.38, N0.5, 2001 ， 746-753.
3. Hongqing Cao, Jingxian Yu, Lishan Kang, Hanxi Yang, Xinping Ai, Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms, Computers and Chemistry 25(2001) 251-259.
• Lishan Kang, Yuanxiang Li, Zhengjun Pan, Jun He and David J. Evans, Massively parallel algorithms from physics and biology, International Journal of Computer Mathematics, Vol.77(2001), 201-250.
• Zeng Sanyou, Kang Lishan, Ding Lixin, A New Evolutionary Algorithm for Constrained Optimization Problem, Computer Science, Vol.26, N0.7,2001, 95-97.
6. Kang Zhuo, Liu Pu, Kang Lishan, Parallel evolutionary modeling for nonlinear ordinary differential equation, Wuhan University Journal of Natural Sciences, Vol.6, No.3 (2001), 659-664.
• Zeng Sanyou, Kang Lishan, Ding Lixin, The evolutionary algorithm based on partial ordering relaton, Computer Engineering, Vol. 27, No.8 (2001), 15-16.
• Kang Lishan, Yan Li, Zhuo Kang, Pu Liu, Yuping Chen, Hugo de Garis, Asynchronous parallel Evolutionary algorithms for Optimization, DCABES 2001 Proceedings, Hubei Science and Technology Press, Wuhan, China, 1-4.
• Lishan Kang, Pu Liu, Yuping Chen, Asynchronous Parallel Evolutionary Algorithm For Function Optimization, Journal of Computer Research & Development ， 2001, 1379-1386.
• Li Yuanxiang, Zou Xiufen, A New Dynamical Evolutionary Algorithm from Statistical Mechanics, Journal of Computer Science and Technology,VOL.18, NO.3, 361-368, 2003 。（ SCI ）
• Xiufen Zou, Yuanxiang Li, Lishan Kang, Zhijian Wu, An Efficient Dynamical Evolutionary Algorithm for Global Optimization, Inter. J. Computer Mathematics, VOL.80, NO.11, 1429-1436, 2003.
• Zeng Sanyou, Ding Lixin, Kang Lishan. A method to approach optimal restoration in image restoration problems without noise energy. Acta Math Scientia, 2003 23(4):536-545(SCIE)
• Liu Yong, Zou Xiufen, From Designing a Single Neural Network to Designing Neural Network Ensemble, Wuhan University J. of Nature Science, VOL.8, NO.1B, 155-164, 2003.
• Liu Yong, Zou Xiufen, Analysis of Negative Correlation Learning, Wuhan University J. of Nature Science, VOL.8, NO.1B, 165-175, 2003.
• Zhijian Wu , Lishan Kang, Xiufen Zou, A parallel global-local mixed evolutionary algorithm for multimodal function optimization[C]. Proceedings of fifth international conference on algorithms and architectures for parallel processing, Beijing , 2002,pp:247-250.(SCIE 收录 )
• Jingzhi Li, Lishan Kang and Zhijian Wu , A self-adaptive neighborhood-based multi-parent crossover operator for real-coded genetic algorithms[C], Proceedings of 2003 Congress on Evolutionary Computation, Canberra, 2003, pp:14-21. (SCIE 收录 )
• Zhijian Wu and Lishan Kang, A fast and elitist parallel evolutionary algorithm for solving systems of non-linear equations[C], Proceedings of 2003 Congress on Evolutionary Computation, Canberra, 2003, pp:1026-1028 。 (SCIE 收录 )
• Lixin Ding; Sanyou Zeng; Lishan Kang ， A fast algorithm on finding the non-dominated set in multi-objective optimization ， Proceedings of 2003 Congress on Evolutionary Computation, Canberra, 2003 ， 2565 – 2571 。
• San You Zeng; LiXin Ding; Yuping Chen; LiShan Kang ， A new multiobjective evolutionary algorithm: OMOEA ， Proceedings of 2003 Congress on Evolutionary Computation, Canberra, 2003 ， 898-905 。
• Aimin Zhou; Lishan Kang; Zhenyu Yan ， Solving dynamic TSP with evolutionary approach in real time ， Proceedings of 2003 Congress on Evolutionary Computation, Canberra, 2003 ， 951 – 957 。
• Yuren Zhou; Yuanxing Li; Jun He; Lishan Kang ， Multi-objective and MGG evolutionary algorithm for constrained optimization ， Proceedings of 2003 Congress on Evolutionary Computation, Canberra, 2003 ， 1 – 5 。
• Hongqing Cao; Jingxian Yu; Lishan Kang ， An evolutionary approach for modeling the equivalent circuit for electrochemical impedance spectroscopy ， Proceedings of 2003 Congress on Evolutionary Computation, Canberra, 2003 ， 1819 – 1825 。
• Chuan Shi; Yan Li; Li-shan Kang ， A new simple and highly efficient multi-objective optimal evolutionary algorithm ， Proceedings of 2003 Congress on Evolutionary Computation, Canberra, 2003 ， 1536 – 1542 。
• 邹秀芬，刘敏忠，康立山， 吴志健 ，解约束多目标优化问题的一种鲁棒的进化算法，计算机研究与发展， 2004, 41(4):985-990.
• Zhijian Wu , Zhilong Tang, Jun Zou, Lishan Kang and Mingbiao Li, Evolutionary algorithm for solving parameter identification problems in elliptic systems[C], Proceedings of 2004 Congress on Evolutionary Computation, USA, 2004.pp:803-808. (EI 和 SCIE 收录 )
• 曹宏庆 ，康立山，陈毓屏．动态系统的常微分方程组建模 ¾ 基于不同搜索技术的实验研究．计算机研究与发展． 2001, 38(6): 746-753 ．
• 周育人，李元香， Pareto 强度值演化算法求解约束优化问题，软件学报， VOL.14, NO.7, 1243-1249, 2003.
• 曾三友 , 康立山 , 丁立新 . 一种基于正则化方法的准最佳图象恢复技术。软件学报， 2003 ， 14 （ 3 ）： 689-696(EI)
• Yuanxiang Li, Analysis of Optimal Trajectory on Evolutionary Algorithm and Some Control Strategies, IEEE World Congress on Computational Intelligence, Hawaii, USA, 2002, IEEE Computer Society Press, pp. 558-603.(EI 、 ISTP)
• Xiufen Zou, Lishang Kang, Yuanxiang Li, A Dynamical Evolutionary Algorithm for Constrained Optimization Problem, IEEE World Congress on Computational Intelligence, Hawaii, USA, 2002, IEEE Computer Society Press, pp. 890-895.(EI 、 ISTP)
• Yuanxiang Li, Xiufen Zou, Solving Global Optimal problems by Using a Dynamical Evolutionary Algorithm, 2002 5 th Intern. Conference on Algorithms and Architectures for Parallel Processing, 2002.10 Beijing, China, IEEE Computer Society Press, pp.170-173.(EI 、 ISTP)
• Yuanxiang Li, Dongmei Liu, Mingzhao Yu, A Genetic Algorithm for Task Scheduling in Network Computing Environment, 2002 5 th Intern. Conference on Algorithms and Architectures for Parallel Processing, 2002.10 Beijing, China, IEEE Computer Society Press, pp.126-129.(EI 、 ISTP)
• 董文永，李元香，二次演化建模在实时仿真中的应用，计算机研究与发展， Vol.25, No.10, 2002, pp.1261-1268.(EI)
• 董文永，李元香，演化仿真优化的并行实现及其应用，计算机学报， Vol.25, No.11, 2002, pp.1236-1242.(EI)
• 夏学文，涂航，李元香 , 一种基于 PSpice 仿真的离线演化硬件的方法 , 武汉大学学报信息科学版， Vol.29 ， NO.8, 35-36, 2004 。
• 王峰，涂航，李元香，樊媛媛。 一种基于遗传程序设计的硬件电路演化方法 , 武汉大学学报信息科学版， Vol.29, No.8, 47-48, 2004 。
• 肖剑波，李元香 , 基于 XML 的 Petri 网在电路仿真中的应用。 武汉大学学报信息科学版， Vol.29 ， NO.8, 86-87 ， 2004 。
• 何国良 , 李元香 , 多个粒子参与交叉的一种动态演化算法 , 计算机工程与应用， Vol.40, NO.8, 83-85, 2004 。
• 张振林，樊媛媛，涂航，李元香 , 遗传程序设计在演化硬件中的应用 , 武汉大学学报信息科学版， Vol.28 ， NO.12 ， 194-195, 2003 。
• 孟庆锋，涂航 , 一种函数级离线演化硬件的实现方案 , 计算机工程， NO.4 ， 75-79 ， 2005 。
• 邓莉丽，章凯，李元香 , 演化硬件设计对象式硬件描述语言。 武汉大学学报信息科学版，， Vol.28, 188-189,2003 。
• 罗涛，李元香 , 自动设计细胞自动机规则表的演化硬件 , 武汉大学学报信息科学版， Vol.28, 174-176, 2003 。
3.3 Award
• Modeling of Evolutionary Computation and Complex System, Natural Science Prize of Hubei Province (Second Class), 2002.
• Parallel Evolutionary Optimization and Modeling Algorithm, Natural Science Prize of Hubei Province (First Class), 2006.