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Publications of Yaochu Jin by Topic

Citations according to Google Scholar (h-index: 28, highest single paper citations: 328. Date: 06.2010)
This page will no longer be maintained. Sorry. For my publications by category, please go to: my website at the University of Surrey.

Computational Intelligence

Artificial Life, Computational Systems Biology, Morphogenetic Robotics and Morphogenetic Design Computational Cognitive Neuroscience

Evolutionary Optimization in Dynamic and Uncertain Environments

  1. Yaochu Jin, Sanghoun Oh and Moongu Jeon. Incremental approximation of nonlinear constraint functions for evolutionary constrained optimization. CEC 2010 (accepted)

  2. Xin Yu, Yaochu Jin, Ke Tang, and Xin Yao. Robust optimization over time -- A new perspective on dynamic optimization problems. CEC 2010 (accepted)

  3. Dudy Lim, Yaochu Jin, Yew-Soon Ong, and Bernhard Sendhoff. Generalizing surrogate-assisted evolutionary computation. IEEE Transactions on Evolutionary Computation, 14(3):329 - 355, 2010

  4. Dudy Lim, Yew-Soon Ong, Yaochu Jin, and Bernhard Sendhoff. Evolutionary optimization with dynamic fidelity computational models. International Conference on Intelligent Computing, pp.235-242, LNAI 5227, September 15-18, 2008, Shanghai, China

  5. Yi Cao, Yaochu Jin, Michal Kowalczykiewicz and Bernhard Sendhoff. Prediction of convergence dynamics of design performance using differential recurrent neural networks. International Joint Conference on Neural Networks, pp.529-534, 2008, Hong Kong, China

  6. Dudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendhoff, Bu-Sung Lee. Efficient hierarchical parallel genetic algorithms using grid computing. Future Generation Computer Systems -- The International Journal of Grid Computing: Theory, Methods and Applications. 23(4):658--670, 2007 PDF

  7. Shengxiang Yang, Yew-Soon Ong, and Yaochu Jin (editors) Evolutionary Computation in Dynamic and Uncertain Environments , Springer, 2007

  8. Dudy Lim, Yew-Soon Ong, Meng-Hiot Lim, and Yaochu Jin. Single/multi-objective inverse robust evolutionary design methodology in the presence of uncertainty. In: S. Yang, Y.S. Ong, and Y. Jin (eds.), Evolutionary Computation in Dynamic and Uncertain Environments, pp.437-456, Springer, 2007

  9. Lars Graening, Yaochu Jin, Bernhard Sendhoff. Individual-based management of meta-models for evolutionary optimization with applications to three-dimensional blade optimization. In: S. Yang, Y.S. Ong, and Y. Jin (eds.), Evolutionary Computation in Dynamic and Uncertain Environments, pp.225-250, Springer, 2007

  10. Dudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendhoff. A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation. Genetic and Evolutionary Computation Conference (GECCO), pp.1288--1295, July 8-11, 2007, London PDF

  11. Shengxiang Yang, Yew-Soon Ong, and Yaochu Jin. Guest editorial: Special issue on evolutionary computation in dynamic and uncertain environments. Genetic Programming and Evolvable Machines. 7(4):292--294, 2006

  12. Juergen Branke, Yaochu Jin. Guest editorial: Special issue on evolutionary computation in the presence of uncertainty. IEEE Transactions on Evolutionary Computation. 10(4):377--379, 2006

  13. Dudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendhoff, and Bu Sung Lee. Inverse multi-objective robust evolutionary optimization. Genetic Programming and Evolvable Machines. 7(4):383--404, 2006 Also here

  14. Ingo Paenke, Juergen Branke, and Yaochu Jin. Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation. IEEE Transactions on Evolutionary Computation . 10(4):405-420, 2006 ( Authors contributed equally.) Also here.

  15. Dudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendhoff. Trusted evolutionary algorithms. IEEE Congress on Evolutionary Computation, pp.456-463, July 2006, Vancouver

  16. Yaochu Jin and Juergen Branke. Evolutionary optimization in uncertain environments - A survey. IEEE Transactions on Evolutionary Computation, 9(3), 303-317, 2005. Also here.
    Citations according to Google Scholar

  17. Yaochu Jin. A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing, 9(1), 3-12, 2005, Springer
    Citations according to Google Scholar

  18. Michael Huesken, Yaochu Jin and Bernhard Sendhoff. Structure optimization of neural networks for evolutionary design optimization. Soft Computing, 9(1), 21-28, 2005

  19. Rothlauf, F., Branke, J., Codnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romerero, J., Smith, G.D., Squillero, G. (editors). Applications of Evolutionary Computing. LNCS 3449, Springer, March 2005

  20. Yaochu Jin (editor). Knowledge Incorporation in Evolutionary Computation. Springer, Berlin Heidelberg, 2005 Table of Contents(PDF file)

  21. Yaochu Jin, Khaled Rasheed and Sushil Louis. Guest editorial: Special issue on approximation and learning in evolutionary computation, Soft Computing, 9(1), 1-2, 2005

  22. Yaochu Jin. Guest editorial: Special issue on knowledge extraction and incorporation in evolutionary computation. IEEE Transactions Systems, Man, and Cybernetics, Part C: Applications and Reviews, 35(2), 129-130, 2005 Also here

  23. Yaochu Jin, Michael Huesken, Markus Olhofer and Bendhard Sendhoff. Neural networks for fitness approximation in evolutionary optimization. In: Y. Jin (ed.), Knowledge Incorporation in Evolutionary Computation,, pp.281-306, Springer, Berlin Heidelberg, 2005

  24. Lars Gräning, Yaochu Jin, Bernhard Sendhoff. Efficient evolutionary optimization using individual-based evolution control and neural networks: A comparative study. European Symposium on Artificial Neural Networks (ESANN'2005). pp.273-278, Bruges, April 2005

  25. Yaochu Jin, Markus Olhofer, and Bernhard Sendhoff. On evolutionary optimization of large problems with small populations. Int. Conf. on Natural Computation. LNCS, 3611, pp.1145-1154, Springer, Changsha, China, 2005

  26. Raidl, G., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (editors). Applications of Evolutionary Computing. LNCS 3005, Springer, April 2004

  27. Yaochu Jin and Bernhard Sendhoff. Reducing fitness evaluations using clustering techniques and neural network ensembles.(draft) Genetic and Evolutionary Computation Conference. LNCS 3102, Springer, pp. 688-699, Seattle, 2004
    Citations according to Google Scholar

  28. Yaochu Jin and Bernhard Sendhoff. Constructing dynamic test problems using the multi-objective optimization concept. In: Applications of Evolutionary Computing. LNCS 3005, Raidl, G.R.; Cagnoni, S.; Branke, J.; Corne, D.W.; Drechsler, R.; Jin, Y.; Johnson, C.G.; Machado, P.; Marchiori, E.; Rothlauf, F.; Smith, G.D.; Squillero, G. (eds.) pp.525-536, Springer, 2004

  29. Lars Willmes, Thomas Bäck,Yaochu Jin and Bernhard Sendhoff. Comparing neural networks and kriging in fitness approximation in evolutionary optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, Vol.1, pp.663-670, 2003

  30. Yaochu Jin and Bernhard Sendhoff. (Corrected version) Trade-off between performance and robustness: An evolutionary multiobjective approach. In: Proceedings of Second International Conference on Evolutionary Multi-criteria Optimization. LNCS 2632, Springer, pp.237-251, Faro, April 2003
    Citations according to Google Scholar

  31. Yaochu Jin, Michael Huesken and Bernhard Sendhoff. Quality measures for approximate models in evolutionary computation. In: Proceedings of the GECCO Workshop on "Learning, Adaptation and Approximation in Evolutionary Computation", pp.170-174, Chicago, 2003 (not peer reviewed)

  32. Yaochu Jin, Markus Olhofer and Bernhard Sendhoff. A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation, 6(5), 481-494, 2002
    Citations according to Google Scholar

  33. Markus Olhofer, Toshiyuki Arima, Yaochu Jin, Toyotaka Sonoda and Bernhard Sendhoff. Optimization of transonic gas turbine blades with evolution strategies. Honda R&D Technical Review, 14(1), 203-216, 2002

  34. Yaochu Jin. Fitness approximation in evolutionary computation - A survey. In: Proceedings of Genetic and Evolutionary Computation Conference, pp.1105-1112, New York, July 2002.
    Citations according to Google Scholar

  35. Yaochu Jin, Markus Olhofer and Bernhard Sendhoff. Managing approximate models in evolutionary aerodynamic design optimization. In: Proceedings of IEEE Congress on Evolutionary Computation, vol.1, pp.592-599. Seoul, Korea, May 2001
    Citations according to Google Scholar

  36. Markus Olhofer, Yaochu Jin and Bernhard Sendhoff. Adaptive encoding for aerodynamic shape optimization using evolution strategies. In: Proceedings of IEEE Congress on Evolutionary Computation, vol.1, pp.576-583, Seoul, Korea, May 2001

  37. Yaochu Jin, Markus Olhofer and Bernhard Sendhoff. On evolutionary optimization with approximate fitness functions. In: Proceedings of the Genetic and Evolutionary Computation Conference GECCO, Las Vegas, Nevada, USA. pp.786- 793, July 10-12, 2000.

    38. [return to top]

Evolutionary Multi-Objective Optimization

  1. Yaochu Jin and Bernhard Sendhoff. A systems approach to evolutionary multi-objective structural optimization and beyond. IEEE Computational Intelligence Magazine, 4(3):62-76, 2009 (Invited feature article)

  2. Aimin Zhou, Qingfu Zhang, Yaochu Jin. Approximating the set of Pareto-optimal solutions in both decision and objective spaces by an estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation, 13(5):1167-1189, 2009

  3. Tatsuya Okabe, Yaochu Jin, Bernhard Sendhoff. Combination of Genetic Algorithms and Evolution Strategies with Self-adaptive Switching. In: Goh, Chi-Keong; Ong, Yew-Soon; Tan, Kay Chen (Eds.), Multi-Objective Memetic Algorithms. pp. 281-307, Springer, 2009

  4. Qingfu Zhang, Aimin Zhou, Yaochu Jin. RM-MEDA: A regularity model based multi-objective estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation, 12(1):41--63, 2008. Also here. ( Errata: Correction of Fig.20 )

  5. Yaochu Jin, Aimin Zhou, Qingfu Zhang, Bernhard Sendhoff, and Edward Tsang. Modeling regularity to improve scalability of model-based multi-objective optimization algorithms. In: J. Knowles, D. Corne, K. Deb (eds.), Multi-Objective Problem Solving from Nature, pp. 331-356, Springer, 2008

  6. Aimin Zhou, Qingfu Zhang, Yaochu Jin and Bernhard Sendhoff. Combination of EDA and DE for continuous biobjective optimization. Congress on Evolutionary Computation, pp.1447-1454, 2008, Hong Kong, China

  7. Aimin Zhou, Qingfu Zhang, Yaochu Jin, Bernhard Sendhoff. Adaptive modeling strategy for continuous multi-objective optimization. Congress on Evolutionary Computation, pp.431--437, Singapore, September 2007 PDF

  8. Aimin Zhou, Qingfu Zhang, Yaochu Jin, Bernhard Sendhoff and Edward Tsang. Global multi-objective optimization via estimation of distribution algorithm with biased initialization and crossover. Genetic and Evolutionary Computation Conference (GECCO), pp.617--623, July 8-11, 2007, London PDF

  9. Aimin Zhou, Yaochu Jin, Qingfu Zhang, Bernhard Sendhoff, Edward Tsang. Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization. The Fourth International Conference on Evolutionary Multi-Criterion Optimization, LNCS 4403, pp. 832--846, March 5-8, 2007, Matsushima, Japan. PDF

  10. Kwasi Foli, Tatsuya Okabe, Markus Olhofer, Yaochu Jin, and Bernhard Sendhoff. Optimization of micro heat exchanger: CFD, analytical approaches and multi-objective evolutionary algorithms. International Journal of Heat and Mass Transfer. 49:1090-1099, 2006

  11. Aimin Zhou, Qingfu Zhang, Yaochu Jin, Bernhard Sendhoff, Edward Tsang. Modeling the population distribution in multi-objective optimization by generative topographic mapping. Parallel Problem Solving from Nature IX, LNCS 4193, pp.443-452, 2006, Reykjavik, Iceland PDF

  12. Aimin Zhou, Yaochu Jin, Qingfu Zhang, Bernhard Sendhoff, Edward Tsang. Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion. IEEE Congress on Evolutionary Computation, pp.3234-3240, July 2006, Vancouver

  13. Aimin Zhou, Qingfu Zhang, Yaochu Jin, Edward Tsang, Tatsuya Okabe. A model-based evolutionary algorithm for bi-objective optimization. Congress on Evolutionary Computation, pp.2568-2575, Edinburgh, September 2005

  14. Tatsuya Okabe, Yaochu Jin, Markus Olhofer, and Bernhard Sendhoff. On test functions for evolutionary multi-objective optimization. Parallel Problem Solving from Nature, VIII, LNCS 3242, Springer, pp.792-802, September 2004

  15. Tatsuya Okabe, Yaochu Jin, Bernhard Sendhoff and Markus Olhofer. Voronoi-based estimation of distribution algorithm for multi-objective optimization. CEC'04, pp. 1594-1602, Portland, 2004

  16. Yaochu Jin and Bernhard Sendhoff. Connectedness, regularity and the success of local search in evolutionary multi-objective optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, Vol.3, pp.1910-1917, 2003

  17. Tatsuya Okabe, Yaochu Jin and Bernhard Sendhoff. A critical survey of performance indices for multi-objective optimization.In: Proceedings of the IEEE Congress on Evolutionary Computation, Vol.2, pp.878-885, 2003
    Citations according to Google Scholar

  18. Tatsuya Okabe, Kwasi Foli, Markus Olhofer, Yaochu Jin and Bernhard Sendhoff. Comparative studies on micro heat exchanger optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, Vol.1, pp.647-654, 2003

  19. Tatsuya Okabe, Yaochu Jin and Bernhard Sendhoff. Evolutionary multi-objective optimization with a hybrid representation.In: Proceedings of the IEEE Congress on Evolutionary Computation , Vol.4, pp. 2262-2269, 2003

  20. Yaochu Jin, Tatsuya Okabe and Bernhard Sendhoff. Solving three-objective optimization problems using evolutionary dynamic weighted aggregation: Results and analysis. In: Proceedings of Genetic and Evolutionary Computation Conference. pp.636, Chicago, 2003

  21. Yaochu Jin and Bernhard Sendhoff. Fuzzy preference incorporation into evolutionary multi-objective optimization. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, Vol.1, pp.26-30, Singapore, Nov. 2002

  22. Yaochu Jin and Bernhard Sendhoff. Incorporation of fuzzy preferences into evolutionary multiobjective optimization. In: Proceedings of Genetic and Evolutionary Computation Conference, pp.683, New York, July 2002.

  23. Yaochu Jin, Markus Olhofer and Bernhard Sendhoff. Dynamic weighted aggregation for evolutionary multi-objective optimization: Why does it work and how? In: Proceedings of Genetic and Evolutionary Computation Conference, pp.1042-1049, San Francisco, USA, 2001.
    Citations according to Google Scholar
    A more detailed description with applications can be found in the unpublished manuscript: Effectiveness of weighted sum of the objectives for evolutionary multi-objective optimization: Methods, analysis and applications. Unpublished manuscript. 2002

  24. Yaochu Jin, Tatsuya Okabe and Bernhard Sendhoff. Adapting weighted aggregation for multiobjective evolution strategies. In: Proceedings of the First International Conference on Evolutionary Multi-criterion Optimization. LNCS 1993, Springer, pp.96-110, Zurich, Switzerland, March 7-9, 2001.
    Citations according to Google Scholar

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Multi-Objective Machine Learning

  1. Yaochu Jin, Robin Gruna, Bernhard Sendhoff. Pareto analysis of evolutionary and learning systems. Frontiers of Computer Science in China, 3(1):4-17, 2009

  2. Yaochu Jin, Bernhard Sendhoff. Pareto-based multi-objective machine learning: An overview and case studies. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 38(3):397-415, 2008. Also here.

  3. Yaochu Jin, Bernhard Sendhoff, Edgar Körner. Rule extraction from compact Pareto-optimal neural networks. In: Multi-objective Evolutionary Algorithms for Knowledge Discovery from Databases, A. Ghosh et al (eds.), pp. 71-90, Springer, 2008

  4. Yaochu Jin, Ruojing Wen, and Bernhard Sendhoff. Evolutionary multi-objective optimization of spiking neural networks. International Conference on Neural Networks, LNCS 4668, 1:370--379, 2007 PDF

  5. Yaochu Jin (editor). Multi-Objective Machine Learning. Springer, Berlin Heidelberg. 2006

  6. Yaochu Jin, Bernhard Sendhoff, Edgar Körner. Simultaneous generation of accurate and interpretable neural network classifiers. In: Multi-Objective Machine Learning, Y. Jin (ed.), pp.281-300, Springer, Berlin Heidelberg, 2006

  7. Hanli Wang, Sam Kwong, Yaochu Jin, Wei Wei and K. Man. A multi-objective hierarchical genetic algorithm for interpretable rule-based knowledge extraction. Fuzzy Sets and Systems, 149(1), 149-186, 2005
    Citations according to Google Scholar

  8. Hanli Wang, Sam Kwong, Yaochu Jin, Wei Wei and K. Man. Agent-based evolutionary approach to interpretable rule-based knowledge extraction. IEEE Transactions Systems, Man, and Cybernetics, Part C: Applications and Reviews, 35(2), 143-155, 2005

  9. Lars Gräning, Yaochu Jin, Bernhard Sendhoff. Generalization improvement in multi-objective learning. International Joint Conference on Neural Networks, pp.9893-9900, July 2006, Vancouver

  10. Yaochu Jin, Bernhard Sendhoff, and Edgar Körner. Evolutionary multi-objective optimization for simultaneous generation of signal-type and symbol-type representations. The Third International Conference on Evolutionary Multi-Criterion Optimization. LNCS 3410, pp.752-766, Springer, Guanajuato, Mexico, March 9-11, 2005

  11. Yaochu Jin, Tatsuya Okabe and Bernhard Sendhoff. Evolutionary multi-objective approach to constructing neural network ensembles for regression. In: C. Coello Coello (ed.), Applications of Evolutionary Multi-objective Optimization, pp. 653-672. World Scientific, 2004

  12. Yaochu Jin, Tatsuya Okabe and Bernhard Sendhoff. Neural network regularization and ensembling using multi-objective evolutionary algorithms. Congress on Evolutionary Computation, pp.1-8, Portland, 2004
    Citations according to Google Scholar

  13. Yaochu Jin, Bernhard Sendhoff, and Edgar Körner. Evolutionary multi-objective model selection: A step towards smooth transitions between signal-type and symbol-type representations? Workshop on "Soft Computing for Information Mining" within KI 2004, Ulm, September 20-21, 2004 (not peer reviewed)

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Interactions of Evolution and Learning

  1. Minh Nghia Le, Yew Soon Ong, Yaochu Jin, and Bernhard Sendhoff. Lamarkian memetic algorithms: Local optimum and connectivity structure analysis. Memetic Computing, 1(3):175-190, 2009

  2. Ingo Paenke, Yaochu Jin, Juergen Branke. Balancing population and individual level of adaptation in changing environments. Adaptive Behavior, 17(2):153--174, 2009

  3. Ingo Paenke, Juergen Branke, and Yaochu Jin. On the influence of phenotype plasticity on genotype diversity. 2007 IEEE Symposium on Foundations of Computational Intelligence (FOCI) , pp. 33-40, April 1-4, 2007, Honolulu, Hawaii PDF ( Best student paper award)

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Co-Evolution

  1. Vineet Khare, Xin Yao, Bernhard Sendhoff, Yaochu Jin, and Heiko Wersing. Co-evolutionary modular neural networks for automatic problem decomposition. Congress on Evolutionary Computation, pp.2691-2698, Edinburgh, September 2005

    2. [return to top]

Interpretability of Fuzzy Systems, Knowledge Extraction and Incorporation

  1. Lipo Wang and Yaochu Jin (editors), 2005 International Conference on Fuzzy Systems and Knowledge Discovery. Part I and Part II, LNAI 3613 and 3614, Springer, August 2005

  2. Yaochu Jin. Advanced Fuzzy Systems Design and Applications . Physica-Verlag/Springer-Verlag, Heidelberg, 2003 (ISBN: 3-7908-1537-3)Table of Contents (PDF file).

  3. Yaochu Jin and Bernhard Sendhoff, Extracting interpretable fuzzy rules from RBF networks. Neural Processing Letters, 17(2), 149-164, 2003
    Citations according to Google Scholar

  4. Yaochu Jin. Interpretability improvement of RBF-based neuro-fuzzy systems using regularized learning. In: J. Cassilas et al (eds.), Interpretability Issues in Fuzzy Modeling, Chapter 26, pp.605-620, Springer, 2003

  5. Yaochu Jin. Generating distinguishable, complete, consistent and compact fuzzy systems using evolutionary algorithms. In: J. Cassilas et al (eds.), Accuracy Improvements in Fuzzy Modeling , Chapter 5, pp.100-118, Springer, 2003.

  6. Yaochu Jin, Knowledge in evolutionary and learning systems (Published dissertation). Institute for Neuroinformatics, Ruhr-University Bochum, Germany. Shaker Verlag, Aachen, 2002. (ISBN: 3-8265-9749-4)

  7. Yaochu Jin. Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement. IEEE Transactions on Fuzzy Systems, 8(2), 212-221, 2000.
    Highly cited article (top 1% within its field) according to Essential Science Indicators (SM) of Thomson ISI
    Citations according to Google Scholar

  8. Yaochu Jin and Bernhard Sendhoff. Knowledge incorporation into neural networks from fuzzy rules. Neural Processing Letters, 10(3), 231-242, 1999.

  9. Yaochu Jin, Werner von Seelen and Bernhard Sendhoff. On generating FC3(flexible, complete, consistent and compact) fuzzy rule systems from data using evolution strategies. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 29(6), 829-845, 1999.
    Citations according to Google Scholar

  10. Yaochu Jin, Werner von Seelen and Bernhard Sendhoff. An approach to rule-based knowledge extraction. In: Proceedings IEEE International Conference on Fuzzy Systems, Anchorage, Alaska, pp.1188-1193, 1998.
    Citations according to Google Scholar

    11. [return to top]

Intelligent Control, Neurofuzzy Control

  1. Sam Kwong and Yaochu Jin. Guest editorial: Special issue on soft computing techniques in intelligent vehicle systems. IEEE Transactions on Industrial Electronics , 50(1), 2-3, 2003. Also here

  2. Yaochu Jin and Werner von Seelen. Evaluating flexible fuzzy controllers via evolution strategies. Fuzzy Sets and Systems, 108, 243-252, 1999
    Citations according to Google Scholar

  3. Yaochu Jin and Jingping Jiang. Techniques in neural network based fuzzy system identification and their applications to control of complex systems. In: Cornelius T. Leondes(ed.), Fuzzy Theory Systems: Techniques and Applications, Chapter 5, pp.112-128, Vol. 1, Academic Press, San Diego, USA, 1999.

  4. Yaochu Jin. Decentralized adaptive fuzzy control of robot manipulators. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 28(1), 47-57, 1998
    Citations according to Google Scholar

  5. Yaochu Jin and Wang Jie. Intelligent Control: Theory and Applications, Henan Science and Technology Publishing House, Zhengzhou, China, 1997 (in Chinese) (ISBN: 7-5349-1983-5)
    (This book is based mainly on the textbook I prepared for a graduate course "Intelligent Control" at the Department of Electrical Engineering, Zhejiang University during 1993-1994)

  6. Yaochu Jin, Jingping Jiang. Performance analysis of fuzzy controllers based on genetic algorithms. Pattern Recognition and Artificial Intelligence 10(1):75-80, 1997 (in Chinese)

  7. Yaochu Jin, Jingping Jiang. Two approaches to fuzzy optimal control. Proceedings of Chinese Society of Electrical Engineering. 16(3):201-204, 1996 (in Chinese)

  8. Yaochu Jin and Jingping Jiang. Optimization of fuzzy control rules by means of genetic algorithms. Control and Decision, 11(6):672-676, 1996 (in Chinese)

  9. Yaochu Jin, Jingping Jiang and Jing Zhu. Neural network based fuzzy identification and its applications to modeling and control of complex systems. IEEE Transactions on Systems, Man and Cybernetics, 25(6), 990-997, 1995
    Citations according to Google Scholar

  10. Yaochu Jin, Jing Zhu and Jingping Jiang. Adaptive fuzzy identification with applications. International Journal of Systems Science,6(2), 197-212, 1995

  11. Yaochu Jin, Jing Zhu. Neural network based fuzzy modeling and its simulation techniques. Journal of Systems Simulation, 7(2):46-55, 1995 (in Chinese)

  12. Yaochu Jin. Design and analysis of fuzzy controllers. In: J. Zhu (Ed.), Fuzzy Control, Chapter 5, pp.240-312, Mechanics Industry Press, Beijing, China, 1995 (in Chinese)

  13. Yaochu Jin, Jingping Jiang. Neuro-fuzzy control of robot manipulators. Chinese Journal of Robot. 17(3):157-163, 1995 (in Chinese)

  14. Yaochu Jin, Jingping Jiang. A neural network model with applications. Journal of Zhejiang University, 29(3):340-347, 1995 (in Chinese)

  15. Yaochu Jin, Jingping Jiang. Fuzzy logic integrated multivariable adaptive neuro-control. Information and Control, 23(4):223-228, 1994 (in Chinese)

  16. Yaochu Jin, Jing Zhu. Neural network based self-learning fuzzy control. Chinese Journal of Eletronics Technology, 4:35-40, 1994 (in Chinese)

  17. Yaochu Jin, Jing Zhu and Jingping Jiang. State estimation and adaptive control of multivariable systems using fuzzy logic and neural networks. AMSE Advances in Modeling and Analysis, 43(2), 1994.

  18. Yaochu Jin, Xiaodong Shen. Two-level hierarchical intelligent fuzzy control of servo systems. Journal of Zhejiang University, 28(6):644-654, 1994 (in Chinese)

  19. Yaochu Jin, Jing Zhu and Jingping Jiang. Fuzzy linearization of nonlinear systems. In: Proceedings IEEE International Conference on Fuzzy Systems, pp.1688-1672, Orlando, Florida, USA, 1994

  20. Yaochu Jin and Jingping Jiang. A novel paradigm of nonlinear system control with applications. In :Proceedings IFAC Symposium on Robot Control, Capri, Italy, 1994

  21. Yaochu Jin, Jingping Jiang. Fuzzy identification with neural networks. In: Proceedings of the Chinese National Conference on Decision and Control. Xiamen, May, 1994 (in Chinese)

  22. Yaochu Jin, Jingping Jiang. Adaptive fuzzy prediction with application to weather forecast. Pattern Recognition and Artificial Intelligence, 6(4):283-290, 1993 (in Chinese)

  23. Yaochu Jin, Jingping Jiang. Neural network based non-linear feedback control. Journal of Zhejiang University, 27, 1993 (in Chinese)

  24. Yaochu Jin and Jingping Jiang. Implemening self-organizing fuzzy controllers with hybrid Pi-Sigma neural networks. In: The Third International Workshop on Advanced Motion Control, Berkeley, California, USA, 1993

  25. Yaochu Jin, Jingping Jiang. Fuzzy logic intergrated variable structure control of a class of nonlinear systems. Control and Decision, 7(1):36-40, 1992 (in Chinese)

  26. Yaochu Jin, Jingping Jiang. Artificial neural networks in robot control- A survey. Robot, 14(6):54-58, 1992 (in Chinese)

  27. Yaochu Jin, Jingping Jiang. Neural network as fuzzy membership functions in neural fuzzy systems. In: Proceedings of the Chinese National Youth Conference on Robotics and Automation. Harbin, August, 1992 (in Chinese)

  28. Yaochu Jin, Jingping Jiang. A novel robust adaptive control of robot manipulators. In: Proceedings of the Chinese National Conference on Robotics and Automation, Harbin, Oct., 1990 (in Chinese)

    29. [return to top]

Brain-Body Co-Evolution, Computational Modeling of Morphogenesis

  1. Till Steiner, Bernhard Sendhoff, and Yaochu Jin. Evolving heterochrony for cellular differentiation using vector field embryogeny. GECCO 2010 (accepted as a full paper)

  2. Ben Jones, Yaochu Jin, Bernhard Sendhoff, and Xin Yao. Emerged optimal distribution of computational workload in the evolution of an undulatory animat. The 11th International Conference on Simulation of Adaptive Behaviors (SAB 2010), August 24-28, 2010

  3. Benjamin Inden, Yaochu Jin, Robert Haschke, Helge Ritter. NEATfields: Evolution of neural fields for visual discrimination and multiple pole balancing tasks. GECCO 2010 (accepted as a poster)

  4. Till Steiner, Yaochu Jin, and Bernhard Sendhoff. Vector field embryogeny. PLoS ONE, 4(12): e8177. doi:10.1371/journal.pone.0008177, 2009

  5. Ben Jones, Yaochu Jin, Bernhard Sendhoff, and Xin Yao. The evolutionary emergence of neural organization in a hydra-like animat. Frontiers in Computational Cognitive Neuroscience, Conference Abstract:Bernstein Conference on Computational Neuroscience, doi: 10.3389/conf.neuro.10.2009.14.057, 2009

  6. Ben Jones, Yaochu Jin, Bernhard Sendhoff, and Xin Yao. The effect of proprioceptive feedback on the distribution of sensory information in a model of an undulating organism. 10th European Conference on Artificial Life, Budapest, September 2009 (accepted)

  7. Lisa Schramm, Yaochu Jin, Bernhard Sendhoff. Emerged coupling of motor control and morphological development in evolution of multi-cellular animates. 10th European Conference on Artificial Life, Budapest, September 2009 (accepted)

  8. Yaochu Jin and Lipo Wang (editors), Fuzzy Systems in Bioinformatics and Computational Biology. Springer, 2009

  9. Yaochu Jin, Lisa Schramm, and Bernhard Sendhoff. A gene regulatory model for the development of primitive nervous systems. INNS-NNN Symposia on Modeling the Brain and Nervous Systems, November 2008, Auckland, New Zealand. LNCS 5506, pp.48-55, 2009

  10. Ben Jones, Yaochu Jin, Xin Yao, and Bernhard Sendhoff. Evolution of neural organization in a Hydra-like animat. 15th Int. Conf. on Neural Information Processing of the Asia-Pacific Neural Network Assembly (ICONIP'08), November 2008, Auckland, New Zealand, LNCS 5506, pp. 216-223, 2009

  11. Ben Jones, Yaochu Jin, Bernhard Sendhoff, and Xin Yao. Evolving functional symmetry in a three dimensional model of an elongated organism. Artificial Life XI, pp.305-312, 2008 PDF

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Morphogenetic Robotics, Morphogenetic Design

  1. Yan Meng and Yaochu Jin. Distributed multi-agent systems for a collective construction task based on virtual swarm intelligence. International Journal of Swarm Intelligence Research, 2010 (accepted)

  2. Yan Meng, Yuyang Zheng, and Yaochu Jin. Autonomous self-reconfiguration of modular robots by evolving a hierarchical mechnochemical model. IEEE Computational Intelligence Magazine, 2010 (accepted)

  3. Hongliang Guo, Yan Meng, and Yaochu Jin. Analysis of local communication load in shape formation of a distributed morphogenetic swarm robotic system. CEC 2010 (accepted)

  4. Yaochu Jin and Jennifer Hallinan. Guest editorial: Special section on evolving gene regulatory networks. BioSystems, 98(3), pages vi-vii, 2009

  5. Hongliang Guo, Yan Meng, and Yaochu Jin. A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network. BioSystems, 98(3):193-203, 2009

  6. Yaochu Jin, Hongliang Guo and Yan Meng. Robustness analysis and failure recovery of a bio-inspired self-organizing multi-robot system. In: Third IEEE International Conference on Self-Adaptive and Self-organizing Systems. IEEE Press, pages 154--164, 2009

  7. Till Steiner, Jens Trommler, Martin Brenn, Yaochu Jin, and Bernhard Sendhoff. Global shape with morphogen gradients and motile polarized cells. Congress on Evolutionary Computation, pp.2225-2232, May 2009, Trondheim, Norway

  8. Hongliang Guo, Yan Meng, Yaochu Jin. Self-adaptive multi-robot construction using gene regulatory networks. IEEE Symposium on Artificial Life (IEEE-ALIFE), pp. 53-60, March 30 - April 1, 2009, Nashville, TN, USA

  9. Till Steiner, Yaochu Jin and Bernhard Sendhoff. A cellular model for evolutionary development of lightweight materials with an inner structure. Genetic and Evolutionary Computation Conference (GECCO), pp.851-858, 2008 ( Best paper nomination)

  10. Till Steiner, Lisa Schramm, Yaochu Jin, Bernhard Sendhoff. Emergence of feedback in artificial gene regulatory networks. Congress on Evolutionary Computation, pp.867--874, Singapore, September 2007 ( 10 finalist for best paper award)
    Available upon request.

  11. Anna Buczak, Yaochu Jin, Houshang Darabi and Mohsen Jafari. Genetic algorithm based sensor network optimization for target tracking. Intelligent Engineering Systems through Artificial Neural Networks, C.H.Dagli, A. Buczak, J. Ghosh, M. Embrechs and O. Ersoy(eds.), Vol. 9, pp.349-354, 1999.

  12. Richard Burne, Anna Buczak, Yaochu Jin, Vikram Jamalabad, Ivan Kadar and Eitan Eadan. A self-organizing, cooperative sensor network for remote surveillance: Current results. SPIE Proceedings, Unattended Ground Sensor Technologies and Applications., Edward Carapezza, David Law and Terry Stalker(eds.), Vol.3713, pp.238-248, 1999.

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Robustness, Evolvability and Evolutionary Dynamics

  1. Yaochu Jin and Jens Trommler. A fitness-independent evolvability measure for evolutionary developmental systems. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp.69-76, Montreal, Canada, May 2-5 2010 PDF ( Best Paper Award )

  2. Yaochu Jin, Yan Meng, and Bernhard Sendhoff. Evolvability and robustness of in silico evolution of gene regulatory dynamics. In: Foundations of Systems Biology in Engineering. Omnipress, pages 68--71, 2009

  3. Till Steiner, Yaochu Jin, Lisa Schramm, and Bernhard Sendhoff. Dynamic links and evolutionary history in simulated gene regulatory networks. In: S. Das et al (eds.), Computational Methodologies in Gene Regulatory Networks, Chapter 21, pp.498-522, 2009

  4. Yaochu Jin and Bernhard Sendhoff. Fuzzy logic in evolving in silico oscillatory dynamics for gene regulatory networks. In: Y. Jin and L. Wang (eds.). Fuzzy Systems in Bioinformatics and Computational Biology, pp.315-327, Springer, 2009

  5. Yaochu Jin, Yan Meng, Bernhard Sendhoff. Influence of regulation logic on the easiness of evolving sustained oscillation for gene regulatory networks. IEEE Symposium on Artificial Life (IEEE-ALIFE), pp.61-68, March 30 - April 1, 2009, Nashville, TN, USA

  6. Yaochu Jin, Robin Gruna, Ingo Paenke, and Bernhard Sendhoff. Multi-objective optimization of robustness and innovation in redundant genetic representations. IEEE Symposium on Multi-Criteria Decision-Making, pp.38-45, March 30 - April 1, 2009, Nashville, TN, USA

  7. Yaochu Jin, Bernhard Sendhoff. Evolving in silico bistable and oscillatory dynamics for gene regulatory network motifs. Congress on Evolutionary Computation , pp.386-391, 2008, Hong Kong, China

  8. Neale Samways, Yaochu Jin, Xin Yao, and Bernhard Sendhoff. Toward a gene regulatory network model for evolving chemotaxis behaviour. Congress on Evolutionary Computation, pp.2574-2581, 2008, Hong Kong, China

  9. Tatsuya Okabe, Yaochu Jin, and Bernhard Sendhoff. Theoretical comparisons of search dynamics of genetic algorithms and evolution strategies. Congress on Evolutionary Computation, pp.382-389, Edinburgh, September 2005

  10. Tatsuya Okabe, Yaochu Jin, and Bernhard Sendhoff. A new approach to dynamics analysis of genetic algorithms without selection. Congress on Evolutionary Computation, pp.374-381, Edinburgh, September 2005

  11. Tatsuya Okabe, Yaochu Jin, Bernhard Sendhoff. On the dynamics of multiobjective optimization. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 247-256, New York, July 2002. ( Best paper nomination)

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Memory Mechanisms, Cognitive Systems, and Brain-Machine Interface

  1. Yuhua Zheng, Yan Meng and Yaochu Jin. Fusing bottom-up and top-down pathways in neural networks for visual object recognition. IJCNN 2010 (accepted)

  2. Yan Meng, Yaochu Jin, Jun Yin, and Matthew Conforth. Human activity detection using spiking neural networks regulated by a gene regulatory network. IJCNN 2010 (accepted)

  3. Andrea Finke, Yaochu Jin, Helge Ritter. A P300 based brain-robot interface for shaping human-robot interaction. Frontiers in Computational Neuroscience, Conference Abstract:Bernstein Conference on Computational Neuroscience, doi: 10.3389/conf.neuro.10.2009.14.108, 2009 (oral presentation)

  4. Martijn Meeter, Rob Veldkamp, Yaochu Jin. Multiple memory stores and operant conditioning: A rationale for memory's complexity. Brain and Cognition, 69(1):200-208, 2009

  5. Yaochu Jin, Bernhard Sendhoff. Alleviating catastrophic forgetting via multi-objective learning. International Joint Conference on Neural Networks, pp.6367-6374, July 2006, Vancouver

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Last update in June, 2009. Please pay attention to the copyrights. For enquiries, contact Yaochu Jin.