Yaochu Jin

Professor Yaochu Jin


Professor in Computational Intelligence
+44 (0)1483 686037
35 BB 02

Biography

Research

Research interests

Research projects

My teaching

My publications

Publications

My verified Google Scholar Citation Profile: h-index = 58, sum of citations = 14,965 (as of 10.2018)
According to Thomson Reuters ISI Web of Science: h-index = 31, sum of citations = 4694 (as of 10.2018)
Computer Scientist National and World Ranking according to Guide2Research.
See also Research Gate, Publications Sorted by Year or DBLP Computer Science Bibliography

Authored Books

Refereed Journal Papers (in English)

  1. L. Zhang, H. Pan, X. Zhang, X. Zeng and Y. Jin. A network reduction based multi-objective evolutionary algorithm for community detection in large-scale complex networks. IEEE Transactions on Cybernetics, 2018 (accepted)
  2. Y. Wang, D. Wang, X. Ye, Y. Wang, Y. Yin, and Y. Jin. A tree ensemble-based two-stage model for advanced-stage colorectal cancer survival prediction. Information Sciences, 2018 (accepted)
  3. H. Wang, and Y. Jin. A random forest assisted evolutionary algorithm for data-driven constrained multi-objective combinatorial optimization of trauma systems. IEEE Transactions on Cybernetics, 2018 (accepted)
  4. Y. Jin, H. Wang, T. Chugh, D. Guo, and K. Miettinen. Data-driven evolutionary optimization: An overview and case studies. IEEE Transactions on Evolutionary Computation, 2018 (accepted)
  5. J. Tian, Y. Tan, J. Zeng, C. Sun, and Y. Jin. Multi-objective infill criterion driven Gaussian process assisted particle swarm optimization of high-dimensional expensive problems. IEEE Transactions on Evolutionary Computation, 2018 (accepted)
  6. Z. Yang, Y. Jin, and K. Hao. A bio-inspired self-learning coevolutionary dynamic multiobjective optimization algorithm for internet of things services. IEEE Transactions on Evolutionary Computation, 2018 (accepted)
  7. Y. Tian, X. Zhang, R. Cheng, C. He, and Y. Jin. Guiding evolutionary multi-objective optimization with generic front modeling. IEEE Transactions on Cybernetics, 2018 (accepted)
  8. Y. Tian, R. Cheng, X. Zhang, Y. Su, and Y. Jin. A strengthened dominance relation considering convergence and diversity for evolutionary many-objective optimization. IEEE Transactions on Evolutionary Computation, 2018 (accepted)
  9. C. Yang, J. Ding, Y. Jin, C. Wang, T. Chai. Multi-tasking multi-objective evolutionary operational indices optimization of beneficiation processes. IEEE Transactions on Automation Science and Engineering, 2018 (accepted).
  10. Z. Yang, Y. Ding, Y. Jin, and K. Hao. Immune-endocrine system inspired hierarchical coevolutionary multiobjective optimization algorithm for IoT service. IEEE Transactions on Cybernetics, 2018 (accepted)
  11. L. Huang, Y. Ding, M. Zhou, Y. Jin, and K. Hao. Multiple-solution optimization strategy for multirobot task allocation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018 (accepted)
  12. X. Peng, Y. Jin, and H. Wang. Multi-modal optimization enhanced cooperative coevolution for large-scale optimization. IEEE Transactions on Cybernetics, 2018 (accepted)
  13. M. Rong, D. Gong, Y. Zhang, Y. Jin, and W. Pedrycz. Multi-directional prediction approach for dynamic multi-objective optimization problems. IEEE Transactions on Cybernetics, 2018 (accepted)
  14. H. Wang, Y. Jin, C. Sun and J. Doherty. Offline data-driven evolutionary optimization using selective surrogate ensembles. IEEE Transactions on Evolutionary Computation, 2018 (accepted)
  15. Y. Hua, Y. Jin and K. Hao. A clustering based adaptive evolutionary algorithm for multi-objective optimization with irregular Pareto fronts. IEEE Transactions on Cybernetics, 2018 (accepted)
  16. W. Du, W. Zhong, Y. Tang, W. Du and Y. Jin. High-dimensional robust multi-objective optimization for order scheduling: A decision variable classification approach, IEEE Transactions on Industrial Informatics, 2018 (accepted)
  17. L. Pan, C. He, Y. Tian, H. Wang, X. Zhang, and Y. Jin. A classification based surrogate-assisted evolutionary algorithm for expensive many-objective optimization. IEEE Transactions on Evolutionary Computation, 2018 (accepted)
  18. J. Ding, C. Yang, Q. Xiao, T. Chai, and Y. Jin. Dynamic evolutionary multi-objective optimization for raw ore allocation in mineral processing. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018 (accepted)
  19. D. Guo, Y. Jin, J. Ding, and T. Chai. Heterogeneous ensemble based infill criterion for evolutionary multi-objective optimization of expensive problems. IEEE Transactions on Cybernetics, 2018 (accepted)
  20. J. Ding, C. Yang, Y. Jin and T. Chai. Generalized multi-tasking for evolutionary optimization of expensive problems. IEEE Transactions on Evolutionary Computation, 2017 (accepted)
  21. Y. Han, D. Gong, Y. Jin, and Q. Pan. Evolutionary multi-objective blocking lot-streaming flow shop scheduling with machine breakdowns. IEEE Transactions on Cybernetics, 2017 (accepted)
  22. H. Wang, Y. Jin and J. Doherty. A generic test suite for evolutionary multi-fidelity optimization. IEEE Transactions on Evolutionary Computation. 2017 (accepted)
  23. X. Ye, J. Li, S. Liu, J. Liang and Y. Jin. A hybrid instance-intensive workflow scheduling method in private cloud environment. Natural Computing, 2016 (accepted)
  24. D.-J. Wang, F. Liu and Y. Jin. A proactive scheduling approach to steel rolling process with stochastic machine breakdown. Natural Computing, 2016 (accepted)
  25. R. Jiao, S. Zeng, C. Li, Y. Jiang and Y. Jin. A complete expected improvement criterion for Gaussian process assisted highly constrained expensive optimization. Information Sciences, 471, 80-96, 2019
  26. Z. Xie and Y. Jin. An extended reinforcement learning framework to model cognitive development with enactive pattern representation. IEEE Transactions on Cognitive and Developmental Systems, 10(3): 738-750, 2018
  27. F. Li, R. Cheng, J. Liu, and Y. Jin. TS-R2EA: A two-stage R2 indicator based evolutionary algorithm for many-objective optimization. Applied Soft Computing, 67: 245-260, 2018
  28. H. Yu, Y. Tan, J. Zeng, C. Sun and Y. Jin. Surrogate-assisted hierarchical particle swarm optimization. Information Sciences, 454-455: 59-72, 2018
  29. Z. Chen, C. K. Yeo, B. S. Lee, C. T. Lau and Y. Jin. Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection. Neurocomputing, 309: 192-200 2018
  30. C. Qian, Y. Yang, K. Tang, Y. Jin, X. Yao, and Z.-H. Zhou. On the effectiveness of sampling for evolutionary optimization in noisy environments. Evolutionary Computation, 26(2):237-267, 2018
  31. C. Sun, J. Ding, J. Zeng and Y. Jin. Fitness approximation assisted competitive swarm optimizer for large scale expensive optimization problems. Memetic Computing, 10(2):123-134 2018.
  32. H. Oh, A. R. Shiraz, Y. Jin. Morphogen diffusion algorithms for tracking and herding using a swarm of Kilobots. Soft Computing, 22(6): 1833-1844, 2018
  33. Y. Tian, R. Cheng, X. Zhang, F. Cheng, and Y. Jin. An indicator based multi-objective evolutionary algorithm with reference point adaptation for better versatility. IEEE Transactions on Evolutionary Computation, 22(1):97-112, 2018
  34. T. Chugh, Y. Jin, K. Miettinen, J. Hakanen, and K. Sindhya. A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization. IEEE Transactions on Evolutionary Computation, 21(1): 129-142, 2018
  35. S. Gu, R. Cheng, Y. Jin. Feature selection for high dimensional classification using a competitive swarm optimizer. Soft Computing, 22(3): 811–822, 2018
  36. X. Zhang, Y. Tian, R. Cheng and Y. Jin. A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization. IEEE Transactions on Evolutionary Computation, 22(1):97 - 112, 2018
  37. X. Zhang, Xi. Zheng, R. Cheng, J. Qiu, and Y. Jin. A competitive mechanism based multi-objective particle swarm optimizer with fast convergence. Information Sciences, 427:63-76, 2018
  38. A. Ramezan Shirazi and Y. Jin. A Strategy for Self-Organized Coordinated Motion of a Swarm of Minimalist Robots. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(5): 326 – 338, 2017
  39. Y. Tian, H. Wang, X. Zhang, and Y. Jin. Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization. Complex & Intelligent Systems, 3(4): 247–263, 2017
  40. H. Wang, M. Olhofer and Y. Jin. Mini-review on preference modeling and articulation in multi-objective optimization: Current status and challenges. Complex & Intelligent Systems, 3(4):233-245, 2017
  41. X. Zhang, F. Duan, L. Zhang, F. Cheng, Y. Jin, K. Tang. Pattern recommendation in task oriented applications: A multi-objective perspective. IEEE Computational Intelligence Magazine, 12(3):43-53, 2017
  42. X. Ye, S. Liu, Y. Yin and Y. Jin. User-oriented many-objective cloud workflow scheduling based on an improved knee point driven evolutionary algorithm. Knowledge Based Systems, 135: 113-124, 2017
  43. R. Cheng, Y. Jin, M. Olhofer and B. Sendhoff. Test problems for large-scale multi- and many-objective optimization. IEEE Transactions on Cybernetics, 7(12): 4108-4121, 2017
  44. Y. Tian, R. Cheng, X. Zhang, and Y. Jin. PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization. IEEE Computational Intelligence Magazine, 12(4):73-87, 2017
  45. C. He, Y. Tian, Y. Jin, X. Zhang, and L. Pan. A radial space division based evolutionary algorithm for many-objective optimization. Applied Soft Computing, 61:603-621, 2017
  46. Y. Liu, D. Gong, J. Sun, and Y. Jin. A many-objective evolutionary algorithm using a one-by-one selection strategy. IEEE Transactions on Cybernetics, 47(9): 2689-2702, 2017
  47. C. Sun, Y. Jin, R. Cheng, J. Ding and J. Zeng. Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems. IEEE Transactions on Evolutionary Computation, 21(4): 644-660, 2017
  48. H. Wang, Y. Jin, and J. Doherty. Committee-based active learning for surrogate-assisted particle swarm optimization of expensive problems. IEEE Transactions on Cybernetics, 47(9): 2664-2677, 2017
  49. S. Gu and Y. Jin. Multi-train: A semi-supervised heterogeneous ensemble classifier. Neurocomputing, 249:202-211, 2017
  50. G. Yao, Y. Ding, Y. Jin, K. Hao. Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system. Soft Computing, 21(15): 4309–4322, 2017
  51. H. Wang, Y. Jin and X. Yao. Diversity assessment in many-objective optimization. IEEE Transactions on Cybernetics, 47(6):1510-1522, 2017 (Highly cited article, as of 28.03.2018)
  52. J. Liu, Y. Chi, C. Zhu and Y. Jin. A time series driven decomposed evolutionary optimization approach for reconstructing large-scale gene regulatory networks based on fuzzy cognitive maps. BMC Bioinformatics, 18:241, 2017. DOI: 10.1186/s12859-017-1657-1
  53. R. Cheng, T. Rodemann, M. Fischer, M. Olhofer, and Y. Jin. Evolutionary many-objective optimization of hybrid electric vehicle control: From general optimization to preference articulation. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(2):97-111, 2017
  54. T. Chugh, N. Chakraborti, K. Sindhya, and Y. Jin. A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem. Materials and Manufacturing Processes, 32(1): 1172-1178, 2017
  55. R. Allmendinger, M. T. M. Emmerich, J. Hakanen, Y. Jin, and E. Rigoni. Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case. Journal of Multi-Criteria Decision Analysis, 14(1/2):5-25, 2017
  56. W. A. Albukhanajer, Y. Jin, and J. A. Briffa. Classifier ensembles for image identification using multi-objective Pareto features. Neurocomputing, 238:316-327, 2017
  57. H. Oh, A. R. Shirazi, C. Sun, and Y. Jin. Bio-inspired self-organising multi-robot pattern formation: A review. Robotics and Autonomous Systems, 91:83-100, 2017
  58. Y. Huang, Y. Ding, K. Hao, and Y. Jin. A multi-objective approach to robust optimization over time considering switching cost. Information Sciences, Vol. 394-395, 183-197, 2017
  59. D.-J. Wang, F. Liu and Y. Jin. A multi-objective evolutionary algorithm guided by directed search for dynamic scheduling. Computers & Operations Research, 79: 279-290, 2017
  60. H. Wang, Y. Jin and J. O. Jansen. Data-driven surrogate-assisted multi-objective evolutionary optimization of a trauma system. IEEE Transactions on Evolutionary Computation, 20(6): 939-952, 2016
  61. C. Brown, Y. Jin, M. Leach and M. Hodgson. m JADE: Adaptive differential evolution with a small population. Soft Computing, 20(10): 4111-4120, 2016
  62. X. Peng, K. Liu and Y. Jin. A dynamic optimization approach to the design of cooperative coevolutionary algorithms. Knowledge-Based Systems, 109: 174-186, 2016
  63. R. Cerri, R. C. Barros, A. C. P. de L. F. Carvalho and Y. Jin. Reduction strategies for hierarchical multi-label classification in protein function prediction. BMC Bioinformatics, 17:373, 2016. DOI: 10.1186/s12859-016-1232-1
  64. X. Zhang, Y. Tian, Y. Jin. Approximate non-dominated sorting for evolutionary many-objective optimization. Information Sciences, 369:14-33, 2016
  65. R. Cheng, Y. Jin, M. Olhofer and B. Sendhoff. A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 20(5):773-791, 2016 (Highly cited article, as of 28.03.2018)
  66. M.-H. Yusoff, J. Chrol-Cannon and Y. Jin. Modeling neural plasticity in echo state networks for classification and regression. Information Sciences, 364–365:184–196, 2016
  67. R. Cheng, Y. Jin, K. Narukawa and B. Sendhoff. A multiobjective evolutionary algorithm using Gaussian process based inverse modeling. IEEE Transactions on Evolutionary Computation, 19(6):761-856, 2015
  68. X. Zhang, Y. Tian and Y. Jin. A knee point driven evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 19(6):761-776, 2015 (Highly cited article, as of 28.03.2018)
  69. D.-J. Wang, F. Liu, Y.-Z. Wang, Y. Jin. A knowledge-based evolutionary proactive scheduling approach in the presence of machine breakdown and deterioration effect. Knowledge-Based Systems. 90:70-80, 2015
  70. Y. Wu, Y. Jin and X. Liu. A directed search strategy for evolutionary dynamic multiobjective optimization. Soft Computing, 19:3221–3235, 2015
  71. W. A. Albukhanajer, J. A. Briffa, and Y. Jin. Evolutionary multi-objective image feature extraction in the presence of noise. IEEE Transactions on Cybernetics, 45(9):1757-1768, 2015
  72. J. Chrol-Cannon and Y. Jin. Learning structure of sensory inputs with synaptic plasticity leads to interference. Frontiers in Computational Neuroscience, 9: 103, 2015. doi: 10.3389/fncom.2015.00103
  73. S. Gu, R. Cheng and Y. Jin. Multi-objective ensemble generation. WIREs Data Mining and Knowledge Discovery, 5(5): 234-245, 2015
  74. X. Zhang, Y. Tian, R. Cheng, and Y. Jin. An efficient approach to non-dominated sorting for evolutionary multi-objective optimization. IEEE Transactions on Evolutionary Computation, 19(2): 201-213, 2015 (Highly cited article, as of 28.03.2018)
  75. T. Rahman, M. Mahapatra, E. Laing and Y. Jin. Evolutionary non-linear modelling for selecting vaccines against antigenically-variable viruses. Bioinformatics, 31(6): 834-840, 2015
  76. B. Yang, Y. Ding, Y. Jin, and K. Hao. Self-organized swarm robot for target search and trapping inspired by bacterial chemotaxis. Robotics and Autonomous Systems, 72: 83-92, 2015
  77. R. Cheng and Y. Jin. A competitive swarm optimizer for large scale optimization. IEEE Transactions on Cybernetics, 45(2): 191-204, 2015 (Highly cited article, as of 28.03.2018)
  78. Y. Jin, Y. Ding, K. Hao, Y. Jin. An endocrine-based intelligent distributed cooperative algorithm for target tracking in wireless sensor networks. Soft Computing, 19(5): 1427-1441, 2015
  79. C. Sun, Y. Jin, J. Zeng and Y. Yu. A two-layer surrogate-assisted particle swarm optimization algorithm. Soft Computing, 19(6): 1461-1475, 2015
  80. T. Zhang, Y. Jin, Y. Ding and K. Hao. A cytokine network inspired cooperative control system for multi-stage stretching processes in fiber production. Soft Computing, 19(6): 1523-1540, 2015
  81. R. Cheng and Y. Jin. A social learning particle swarm optimization algorithm for scalable optimization. Information Sciences, 291:43-60, 2015 (Highly cited article, as of 28.03.2018)
  82. Z.-H. Zhou, N. V. Chawla, Y. Jin, and G. J. Williams. Big data opportunities and challenges: Discussions from data analytics perspectives. IEEE Computational Intelligence Magazine, 9(4):62-74, 2014
  83. J. Chrol-Cannon and Y. Jin. On the correlation between reservoir metrics and performance for time series classification under the influence of synaptic plasticity. PLOS ONE, DOI: 10.1371/journal.pone.0101792, July 10, 2014
  84. S. A. Thomas and Y. Jin. Reconstructing biological gene regulatory networks: Where optimization meets big data. Evolutionary Intelligence, 7(1):29-47, 2014
  85. C. Smith and Y. Jin. Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction. Neurocomputing, 143:302-311, 2014
  86. J. Chrol-Cannon and Y. Jin. Computational modeling of neural plasticity for self-organization of neural networks. BioSystems, 125: 43-54, 2014
  87. A. Zhou, Y. Jin and Q. Zhang. A population prediction strategy for evolutionary dynamic multiobjective optimization. IEEE Transactions on Cybernetics, 44(1): 40-53, 2014
  88. X. Sun, S. Chen, Y. Jin and D. Gong. A new surrogate-assisted interactive genetic algorithm with weighted semi-supervised learning. IEEE Transactions on Cybernetics, 43(2): 685-698, 2013
  89. S. A. Thomas and Y. Jin. Evolving connectivity between genetic oscillators and switches using evolutionary algorithms. Journal of Bioinformatics and Computational Biology, 11(3), 2013
  90. B. Inden, Y. Jin, R. Haschke, H. Ritter, B. Sendhoff. An examination of different fitness and novelty based selection methods for the evolution of neural networks. Soft Computing. 17(5): 753-767, 2013
  91. Y. Jin, K. Tang, X. Yu, B. Sendhoff and X. Yao. A framework for finding robust optimal solutions over time. Memetic Computing, 5(1):3-18, 2013
  92. M.N. Le, Y.S. Ong, S. Menzel, Y. Jin, and B. Sendhoff. Evolution by adapting surrogates. Evolutionary Computation, 21(2):313-340, 2013
  93. Y. Meng, H. Guo and Y. Jin. A morphogenetic approach to flexible and robust shape formation for swarm robotic systems. Robotics and Autonomous Systems, 61(1):25-38, 2013
  94. C. Sun, J. Zeng, J. Pan, S. Xue, Y. Jin. A new fitness estimation strategy for particle swarm optimization. Information Sciences: 221: 355-370, 2013
  95. G. Jia, Y. Wang, Z. Cai, and Y. Jin. An improved (m + l)-constrained differential evolution for constrained optimization. Information Sciences, 222: 302-322, 2013
  96. L. Schramm, Y. Jin, and B. Sendhoff. Evolution and analysis of genetic networks for stable cellular growth and regeneration. Artificial Life, 18(4): 425-444, 2012
  97. D. Bush and Y. Jin. Calcium control of hippocampal STDP. Journal of Computational Neuroscience. 33(3):495-514, 2012
  98. J. Yin, Y. Meng and Y. Jin. A developmental approach to structural self-organization in reservoir computing. IEEE Transactions on Autonomous Mental Development, DOI: 10.1109/TAMD.2012.2182765, 2012
  99. Y. Jin, H. Guo, and Y. Meng. A hierarchical gene regulatory network for adaptive multi-robot pattern formation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42(3):805-816, 2012
  100. M. N. Le, Y. S. Ong, Y. Jin and B. Sendhoff. A unified framework for symbiosis of evolutionary mechanisms with application to water clusters potential model design. IEEE Computational Intelligence Magazine, 7(1):20-35, 2012
  101. B. Inden, Y. Jin, R. Haschke, H. Ritter. Evolving neural fields for problems with large input and output spaces. Neural Networks, 28: 24-39, 2012
  102. H. Guo, Y. Jin, and Y. Meng. A morphogenetic framework for self-organized multi-robot pattern formation and boundary coverage. ACM Transactions on Autonomous and Adaptive Systems, 7(1), Article No. 15, April 2012. Doi:10.1145/2168260.2168275
  103. E. Gehrmann, C. Glaesser, Y. Jin, B. Sendhoff, B. Drossel, and K. Hamacher. Robustness of glycolysis in yeast to internal and external noise. Physical Review E, E 84, 021913, 2011
  104. Y. Meng, Y. Jin and J. Yin. Modeling activity-dependent plasticity in BCM spiking neural networks with application to human behavior recognition. IEEE Transactions on Neural Networks, 22(12):1952-1966, 2011
  105. Y. Zhang, Y. Meng, Y. Jin. Object recognition using a bio-inspired neuron model with bottom-up and top-down pathways. Neurocomputing, 74(17): 3158-3169, 2011
  106. Y. Jin. Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation, 1(2):61-70, 2011 (Highly cited article, as of 28.03.2018)
  107. S. Oh, Y. Jin and M. Jeon. Approximate models for constraint functions in evolutionary constrained optimization. International Journal of Innovative Computing, Information and Control. 7(11):6585-6603, 2011
  108. Y. Jin and Y. Meng. Emergence of robust regulatory motifs from in silico evolution of sustained oscillation. BioSystems, 103(1):38-44, 2011
  109. Y. Jin and Y. Meng. Morphogenetic robotics: An emerging new field in developmental robotics. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 41(2):145-160, 2011
  110. M. Liu, S. Zhang and Y. Jin. Multi-sensor optimal H_¥ fusion filters for delayed nonlinear intelligent systems based on a unified model. Neural Networks, 24(3):280-290, 2011
  111. Y. Meng, Y. Zheng, and Y. Jin. Autonomous self-reconfiguration of modular robots by evolving a hierarchical mechanochemical model. IEEE Computational Intelligence Magazine, 6(1):43-54, 2011
  112. Y. Meng and Y. Jin. Distributed multi-agent systems for a collective construction task based on virtual swarm intelligence. International Journal of Swarm Intelligence Research, 1(2), 58-79, 2010
  113. D. Lim, Y. Jin, Y.-S. Ong, and B. Sendhoff. Generalizing surrogate-assisted evolutionary computation. IEEE Transactions on Evolutionary Computation, 14(3):329-355, 2010
  114. T. Steiner, Y. Jin, and B. Sendhoff. Vector field embryogeny. PLoS ONE, 4(12): e8177. doi:10.1371/journal.pone.0008177, 2009
  115. H. Guo, Y. Meng, and Y. Jin. A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network. BioSystems, 98(3):193-203, 2009
  116. M.N. Le, Y. S. Ong, Y. Jin, and B. Sendhoff. Lamarckian memetic algorithms: Local optimum and connectivity structure analysis. Memetic Computing, 1(3):1795-190, 2009
  117. A. Zhou, Q. Zhang, Y. 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
  118. Y. Jin and B. Sendhoff. A systems approach to evolutionary multi-objective structural optimization. IEEE Computational Intelligence Magazine, 4(3):62-76, 2009
  119. I. Paenke, Y. Jin, J. Branke. Balancing population and individual level of adaptation in changing environments. Adaptive Behavior, 17(2):153-174, 2009
  120. M. Meeter, R. Veldkamp, Y. Jin. Multiple memory stores and operant conditioning: A rationale for memory's complexity. Brain and Cognition, 69(1):200-208, 2009
  121. Y. Jin, R. Grunar, and B. Sendhoff. Pareto analysis of evolutionary and learning systems. Frontiers of Computer Science in China, 3(1):4-17, 2009
  122. Y. Jin and B. 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
  123. Q. Zhang, A. Zhou, Y. Jin. RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm.  IEEE Transactions on Evolutionary Computation. 12(1):41-63, 2008 (Highly cited article, as of 28.03.2018)
  124. D. Lim, Y.-S. Ong, Y. Jin, B. Sendhoff, B.-S. Lee. Efficient hierarchical parallel genetic algorithms using grid computing.  Future Generation Computer Systems. 23(4):658-670, 2007
  125. D. Lim, Y.-S. Ong, Y. Jin, B. Sendhoff, and B. S. Lee. Adaptive inverse multi-objective robust evolutionary design optimization. Genetic Programming and Evolvable Machines. 7(4), 383-404, 2007
  126. I. Paenke, J. Branke, and Y. Jin. Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation. IEEE Transactions on Evolutionary Computation, 10(4), 405-420, 2006
  127. K. Foli, T. Okabe, M. Olhofer, Y. Jin, and B. Sendhoff. Optimization of micro heat exchanger: CFD, analytical approach and multi-objective evolutionary algorithms. International Journal of Heat and Mass Transfer. 49, 1090-1099, 2006
  128. Y. Jin and J. Branke. Evolutionary optimization in uncertain environments - A survey. IEEE Transactions on Evolutionary Computation, 9(3), 303-317, 2005
  129. H. Wang, S. Kwong, Y. Jin, W. Wei and K. Man. Agent-based evolutionary approach to interpretable rule-based knowledge extraction. IEEE Transactions Systems, Man, and Cybernetics, Part C, 29(2), 143-155, 2005
  130. H. Wang, S. Kwong, Y. Jin, W. 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
  131. Y. Jin. A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing, 9(1), 3-12, 2005
  132. M. Huesken, Y. Jin and B. Sendhoff. Structure optimization of neural networks for evolutionary design optimization. Soft Computing, 9(1), 21-28, 2005
  133. Y. Jin and B. Sendhoff, Extracting interpretable fuzzy rules from RBF networks. Neural Processing Letters, 17(2), 149-164, 2003
  134. Y. Jin, M. Olhofer and B. Sendhoff. A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation, 6(5), 481-494, 2002
  135. Y. Jin. Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement. IEEE Transactions on Fuzzy Systems, 8(2), 212-221, 2000
  136. Y. Jin and B. Sendhoff. Knowledge incorporation into neural networks from fuzzy rules. Neural Processing Letters, 10(3), 231-242, 1999
  137. Y. Jin, W. von Seelen and B. Sendhoff. On generating FC3 fuzzy rule systems from data using evolution strategies. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 29(6), 829-845, 1999
  138. Y. Jin and W. von Seelen. Evaluating flexible fuzzy controllers via evolution strategies. Fuzzy Sets and Systems, 108, 243-252, 1999
  139. Y. Jin. Decentralized adaptive fuzzy control of robot manipulators. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 28(1), 47-57, 1998
  140. Y. Jin, J. Jiang and J. 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
  141. Y. Jin, J. Zhu and J. Jiang. Adaptive fuzzy identification with applications. International Journal of Systems Science, 6(2), 197-212, 1995

Peer-Reviewed Conference Papers

  1. C. Sun, Y. Jin and Y. Tan. Semi-supervised learning assisted particle swarm optimization of computationally expensive problems. Genetic and Evolutionary Computation Conference, Kyoto, Japan, 15-19 July 2018
  2. G. Yu, Y. Jin and M. Olhofer. Method for a posteriori identification of knee points based on solution density. Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 8-13 July 2018
  3. H. Wang, J. Doherty and Y. Jin. Hierarchical surrogate-assisted evolutionary multi-scenario airfoil shape optimization. Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 8-13 July 2018
  4. Y. Tian, X. Xiang, X. Zhang, R. Cheng and Y. Jin. Sampling reference points on the Pareto fronts of multi-objective optimization problems. Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 8-13 July 2018
  5. M. G. Carneiro, T. H. Cupertino, R. Cheng, Y. Jin and L. Zhao. Nature-inspired graph optimization for dimensionality reduction. The Annual IEEE International Conference on Tools with Artificial Intelligence, November 6-7, 2017, Boston, MA, USA
  6. S. Thomas, Y. Jin, J. Bunch and I. Gilmore. Enhancing classification of mass spectrometry imaging data with deep neural networks. IEEE Symposium Series on Computational Intelligence, November 27-December 1, 2017, Hawaii, USA
  7. N. Naik, P. Jenkins, R. Cooke, D. Ball, A. Foster, Y. Jin. Augmented windows fuzzy firewall for preventing denial of service attack. FUZZ-IEEE 2017: 1-6
  8. T. Jie, T. Ying, S. Chaoli, Z. Jianchao, Y. Haibo and Y. Jin. Comparisons of different kernels in Kriging-assisted evolutionary expensive optimization. IEEE Symposium Series on Computational Intelligence, November 27-December 1, 2017, Hawaii, USA
  9. C. Yang, J. Ding, K. C. Tan, and Y. Jin. Two-stage assortative mating for multi-objective multifactorial evolutionary optimization. The 56th IEEE Conference on Decision and Control, December 12-15, 2017, Melbourne, Australia
  10. H. Wang and Y. Jin. Efficient nonlinear correlation detection for decomposed search in evolutionary multi-objective optimization. Congress on Evolutionary Computation, June 2017
  11. T. Chugh, K. Sindhya, K. Miettinen, Y. Jin, T. Kratky, and P. Makkonen.  Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system. Congress on Evolutionary Computation, June 2017 (Best Student Paper Award)
  12. D. Guo, T. Chai, J. Ding, and Y. Jin. Small data driven evolutionary multi-objective optimization of fused magnesium furnaces. IEEE Symposium on Computational Intelligence, Athens, Greece, December 2016
  13. J. Hakanen, T. Chugh, K. Sindhya, Y. Jin, K. Miettinen. Connections of reference vectors and different types of preference information in interactive multiobjective evolutionary algorithms. IEEE Symposium on Computational Intelligence, Athens, Greece, December 2016
  14. J. Tian, Y. Tan, C. Sun, J. Zeng, and Y. Jin. A self-adaptive similarity-based fitness approximation for evolutionary optimization. IEEE Symposium on Computational Intelligence, Athens, Greece, December 2016
  15. U. Yolcu, Y. Jin and E. Egrioglu. An ensemble of single multiplicative neuron models for probabilistic prediction. IEEE Symposium on Computational Intelligence, Athens, Greece, December 2016
  16. X. Zhang, Y. Tian, R. Cheng, and Y. Jin. Empirical analysis of a tree-based efficient non-dominated sorting approach for many-objective optimization. IEEE Symposium on Computational Intelligence, Athens, Greece, December 2016
  17. H. Yu, C. Sun, J. Zeng, Y. Tan and Y. Jin. An adaptive model selection strategy for surrogate-assisted particle swarm optimization algorithm. IEEE Symposium on Computational Intelligence, Athens, Greece, December 2016
  18. S. Cheng, B. Liu, Y. Shi, Y. Jin and B. Li. Evolutionary computation and big data: Key challenges and future directions. DMBD 2016, LNCS 9714, pp. 3–14, 2016
  19. M. Carneiro, L. Zhao, R. Cheng and Y. Jin. Network structural optimization based on swarm intelligence for high level classification. International Joint Conference on Neural Networks, Vancouver, July 2016
  20. C. Yang, J. Ding, T. Chai and Y. Jin. Reference point based prediction for evolutionary dynamic multiobjective optimization. Congress on Evolutionary Computation, Vancouver, July 2016
  21. Y. Tian, X. Zhang, R. Cheng and Y. Jin. A multi-objective evolutionary algorithm based on an enhanced inverted generational distance metric. Congress on Evolutionary Computation, Vancouver, July 2016
  22. R. Cheng, M. Olhofer and Y. Jin. Reference vector based a posteriori preference articulation for evolutionary multiobjective optimization. Congress on Evolutionary Computation (CEC 2015). May 24-28, 2015, Sendai, Japan
  23. Y. Huang, Y. Jin and Y. Ding. New performance indicators for robust optimization over time. Congress on Evolutionary Computation (CEC 2015). May 24-28, 2015, Sendai, Japan
  24. W. A. Albukhanajer, Y. Jin and J. Briffa, Trade-off between computational complexity and accuracy in evolutionary image feature extraction. Congress on Evolutionary Computation (CEC 2015). May 24-28, 2015, Sendai, Japan
  25. R. Cheng, Y. Jin and K. Narukawa. Adaptive reference vector generation for inverse model based evolutionary multiobjective optimization with degenerate and disconnected Pareto fronts. The 8th Int. Conf. on Evolutionary Multi-Criterion Optimization (EMO'2015), Guimarães, Portugal
  26. S. Gu and Y. Jin. Generating diverse and accurate classifier ensembles using multi-objective optimization. IEEE Symposium Series on Computational Intelligence, December 9-12, 2014, Orlando, Florida, USA
  27. M. Rustell, A. Orsini, S.T. Khu, Y. Jin and B. Gouldby. (2014). Optimizing an LNG terminal subject to uncertainty. 11th International Conference of Hydroinformatics. New York, 17-20th August 2014.
  28. M.-H. Yusoff and Y. Jin. Modeling neural plasticity in Echo State Networks for time series prediction. 2014 UK Workshop on Computational Intelligence, Bradford, UK, 8 - 10 September 2014
  29. H. Oh and Y. Jin. Adaptive swarm robot region coverage using gene regulatory networks. The 15th Towards Autonomous Robotic Systems, September 1-3, 2014, Birmingham, UK.
  30. A. Ramezan Shirazi, H. Oh and Y. Jin. Morphogenetic self-organization of collective movement without directional sensing. The 15th Towards Autonomous Robotic Systems, September 1-3, 2014, Birmingham, UK
  31. C. Qian, Y. Yu, Y. Jin and Z.-H. Zhou. On the effectiveness of sampling for evolutionary optimization in noisy environments. Parallel Problem Solving from Nature (PPSN’14), September 13-17, 2014 Ljubljana, Slovenia
  32. T. Liu, C. Sun, J. Zeng and Y. Jin. Similarity- and reliability-assisted fitness estimation for particle swarm optimization of expensive problems. IEEE Congress on Evolutionary Computation, July 2014
  33. R. Cheng and Y. Jin. Demonstrator selection in a social learning particle swarm optimizer. IEEE Congress on Evolutionary Computation, July 2014
  34. H. Oh and Y. Jin. Evolving hierarchical gene regulatory networks for morphogenetic pattern formation of swarm robotics. IEEE Congress on Evolutionary Computation, July 2014
  35. W. A. Albukhanajer, Y. Jin and J. A. Briffa. Neural network ensembles for image identification using Pareto-optimal features. IEEE Congress on Evolutionary Computation, July 2014
  36. C. Smith, J. Doherty and Y. Jin. Multi-objective evolutionary recurrent neural network ensemble for prediction of computational fluid dynamic simulations. IEEE Congress on Evolutionary Computation, July 2014 (Runner-up, Best Student Paper Award)
  37. R. Cheng, C. Sun and Y. Jin. A multi-swarm evolutionary framework based on a feedback mechanism. In: IEEE Congress on Evolutionary Computation (CEC'2013), Cancun, Mexico, June 20-23 2013
  38. A. Xiao, B. Wang and Y. Jin. Evolutionary truss layout optimization using the vectorized structure approach. In: IEEE Congress on Evolutionary Computation (CEC'2013), Cancun, Mexico, June 20-23 2013
  39. J. Lu, B. Li, and Y. Jin. An evolution strategy assisted by an ensemble of local Gaussian process models. In: Genetic and Evolutionary Computation Conference (GECCO'2013), Amsterdam, The Netherlands, 6-10 July 2013
  40. C. Smith, J. Doherty, and Y. Jin. Recurrent neural network ensembles for convergence prediction in surrogate-assisted evolutionary optimisation. IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Singapore, 16-19 April 2013
  41. C. Sun, J. Zeng, J. Pan and Y. Jin. Similarity based evolution control for fitness estimation in particle swarm optimisation. IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Singapore, 16-19 April 2013
  42. S. A. Thomas and Y. Jin. Single and multi-objective in silico evolution of tunable genetic oscillators. The 7th Int. Conf. on Evolutionary Multi-Criterion Optimization (EMO 2013), 19-22 March 2013, Sheffield, UK
  43. W. A. Albukhanajer, Y. Jin, J. A. Briffa, and G. Williams. A comparative study of multi-objective evolutionary Trace Transform algorithms for robust feature extraction. The 7th Int. Conf. on Evolutionary Multi-Criterion Optimization (EMO 2013), 19-22 March 2013, Sheffield, UK
  44. S. Gu and Y. Jin. Heterogeneous classifier ensembles for EEG-based motor imaginary detection. 2012 UK Workshop on Computational Intelligence. Edinburgh, September 2012
  45. W. Albukhanajer, Y. Jin, J. Briffa and G. Williams. Evolutionary multi-objective optimization of Trace transform for invariant feature extraction. 2012 IEEE Congress on Evolutionary Computation, Brisbane, Australia, June 2012
  46. J. Chrol-Cannon, A. Gruning and Y. Jin. The emergence of polychronous groups under varying input patterns, plasticity rules and network connectivities. 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, June 2012
  47. S. Thomas and Y. Jin. Combining genetic oscillators and switches using evolutionary algorithms. 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, USA, May 2012
  48. L. Schramm, Y. Jin and B. Sendhoff. Quantitative analysis of redundancy in evolution of developmental systems. 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, USA, May 2012
  49. B. Inden, Y. Jin, R. Haschke and H. Ritter. Exploiting inherent regularity in control of multilegged robot locomotion by evolving neural fields. Third World Congress on Nature and Biologically Inspired Computing (NaBIC2011), October 19-21, 2011, Salamanca University, Spain
  50. D. Bush and Y. Jin. A unified computational model of the genetic regulatory networks underlying synaptic, intrinsic and homeostatic plasticity. 2011 International Conference on Wiring the Brain: Making Connections, April 12-15, 2011, County Wicklow, Ireland
  51. L. Schramm, Y. Jin, and B. Sendhoff. Redundancy creates opportunity in developmental representations. 2011 IEEE Symposium on Artificial Life, Paris, France, April 11-15, 2011
  52. H. Guo, Y. Meng, and Y. Jin. Swarm robot pattern formation using a morphogenetic multi-cellular based self-organization algorithm. 2011 IEEE International Conference on Robotics and Automation (ICRA 2011), Shanghai, China, May 9-13, 2011
  53. Y. Meng, Y. Zhang, A. Sampath, Y. Jin, and B. Sendhoff. Cross-ball: A new morphogenetic self-reconfigurable modular robot. 2011 IEEE International Conference on Robotics and Automation (ICRA 2011), Shanghai, China, May 9-13, 2011
  54. J. Goh, L. Tang, L. Al Turk, Y. Jin, G. Saleh, A combined particle swarm optimisation and genetic algorithm for context analysis of medical images. In: 4th International Conference on Health Informatics. Rome, Italy, 26-29, January, 2011
  55. Y. Jin, Y. Meng and H. Guo. A morphogenetic self-organization algorithm for swarm robotic systems using relative position information. 2010 UK Workshop on Computational Intelligence, Colchester, UK, September 2010
  56. B. Jones, Y. Jin, B. Sendhoff, and X. 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), pp.587-596, August 24-28, 2010
  57. Y. Jin, S. Oh and M. Jeon. Incremental approximation of nonlinear constraint functions for evolutionary constrained optimization. Congress on Evolutionary Computation,  pp.2966-2973, Barcelona, July 2010
  58. X. Yu, Y. Jin, K. Tang, and X. Yao. Robust optimization over time -- A new perspective on dynamic optimization problems. Congress on Evolutionary Computation, pp. 3998-4003, Barcelona, July 2010
  59. Y. Meng, Y. Jin, J. Yin, and M. Conforth. Human activity detection using spiking neural networks regulated by a gene regulatory network. Int. Joint Conference on Neural Networks, pp.2232-2237, Barcelona, July 2010
  60. H. Guo, Y. Meng, and Y. Jin. Analysis of local communication load in shape formation of a distributed morphogenetic swarm robotic system. Congress on Evolutionary Computation, pp. 1117-1124,, Barcelona, July 2010
  61. Y. Zheng, Y. Meng and Y. Jin. Fusing bottom-up and top-down pathways in neural networks for visual object recognition. Int. Joint Conference on Neural Networks, pp.2064-2031, Barcelona, July 2010
  62. T. Steiner, B. Sendhoff, and Y. Jin. Evolving heterochrony for cellular differentiation using vector field embryogeny. Genetic and Evolutionary Computation Conference, pp.571-578, Portland, July 2010
  63. B. Inden, Y. Jin, R. Haschke, H. Ritter. NEATfields: Evolution of neural fields for visual discrimination and multiple pole balancing tasks. Genetic and Evolutionary Computation Conference, pp. 645-646, Portland, July 2010
  64. H. Lex, M. Weigelt, Y. Jin, and T. Schack. Visuo-motor adaptation relies on kinesthetic representation of movement directions. North American Society for Psychology of Sport and Physical Activity (NASPSPA) Conference, Tucson, AZ, June 10-12, 2010 (abstract published in Journal of Sport & Exercise Psychology, 32, 100-101)
  65. Y. Jin and J. 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 (Best Paper Award)
  66. A. Finke, Y. Jin, H. Ritter. A P300 based brain-robot interface for shaping human-robot interaction. Bernstein Conference on Computational Neuroscience, Frankfurt, September 2009. doi: 10.3389/conf.neuro.10.2009.14.108
  67. B. Jones, Y. Jin, B. Sendhoff, and X. Yao. The evolutionary emergence of neural organization in a hydra-like animat. Bernstein Conference on Computational Neuroscience, Frankfurt, September 2009 (poster presentation)
  68. B. Jones, Y. Jin, B. Sendhoff, and X. 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
  69. L. Schramm, Y. 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
  70. Y. Jin, Y. Meng, and B. Sendhoff. Evolvability and robustness of in silico evolution of gene regulatory dynamics. In: Foundations of Systems Biology in Engineering. Omnipress, pages 68-71, 2009
  71. Y. Jin, H. Guo and Y. 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
  72. T. Steiner, J. Trommler, M. Brenn, Y. Jin, and B. Sendhoff. Global shape with morphogen gradients and motile polarized cells. Congress on Evolutionary Computation, pp.2225-2232, May 2009, Trondheim, Norway
  73. Y. Jin, Y. Meng, B. Sendhoff. Influence of regulation logic on the easiness of evolving sustained oscillation for gene regulatory networks. IEEE ALIFE, pp.61-68, March 30 - April 1, 2009, Nashville, TN, USA
  74. H. Guo, Y. Meng, Y. Jin. Self-adaptive multi-robot construction using gene regulatory networks. IEEE ALIFE, pp. 53-60, March 30 - April 1, 2009, Nashville, TN, USA
  75. Y. Jin, R. Gruna, I. Paenke, B. Sendhoff. Multi-objective optimization of robustness and innovation in redundant genetic representations. IEEE MCDM, pp.38-45, March 30 - April 1, 2009, Nashville, TN, USA 2009
  76. Y. Jin, L. Schramm, and B. Sendhoff. A gene regulatory model for the development of primitive nervous systems. INNS-NNN Symposia on Modeling the Brain and Nervous Systems, LNCS 5506, pp.48-55, 2009 (Best paper nomination)
  77. B. Jones, Y. Jin, X. Yao, and B. 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), LNCS 5506, pp. 216-223, 2009
  78. D. Lim, Y.-S. Ong, Y. Jin, and B. Sendhoff. Evolutionary optimization with dynamic fidelity computational models. International Conference on Intelligent Computing, pp.235-242, September 15-18, 2008, Shanghai, China
  79. B. Jones, Y. Jin, B. Sendhoff, and X. Yao. Evolving functional symmetry in a three dimensional model of an elongated organism. Artificial Life IX, Winchester, UK, pp.305-312, August 2008
  80. T. Steiner, Y. Jin and B. Sendhoff. A cellular model for evolutionary development of lightweight materials with an inner structure. Genetic and Evolutionary Computation Conference, pp.851-858, Atlanta, July 2008 (Best paper nomination)
  81. Y. Cao, Y. Jin, M. Kowalczykiewicz and B. Sendhoff. Prediction of convergence dynamics of design performance using differential recurrent neural networks. International Joint Conference on Neural Networks, pp.529-534, Hong Kong, June 2008
  82. A. Zhou, Q. Zhang, Y. Jin and B. Sendhoff. Combination of EDA and DE for continuous bi-objective optimization. Congress on Evolutionary Computation, pp.1447-1454, Hong Kong, June 2008
  83. N. Samways, Y. Jin, X. Yao, and B. Sendhoff. Toward a gene regulatory network model for evolving chemotaxis behavior. Congress on Evolutionary Computation, pp.2574-2581, Hong Kong, June 2008
  84. Y. Jin, B. Sendhoff. Evolving in silico bistable and oscillatory dynamics for gene regulatory network motifs. Congress on Evolutionary Computation, pp.386-391,Hong Kong, June 2008
  85. A. Zhou, Q. Zhang, Y. Jin, B. Sendhoff. Adaptive modeling strategy for continuous multi-objective optimization. Congress on Evolutionary Computation, pp.431-437 Singapore, September 2007
  86. T. Steiner, L. Schramm, Y. Jin, Bernhard Sendhoff. Emergence of feedback in artificial gene regulatory networks. Congress on Evolutionary Computation, pp.867-874, September 2007 (Best paper 10 finalist)
  87. Y. Jin, R.Wen, B. Sendhoff. Evolutionary multi-objective optimization of spiking neural networks. International Conference on Artificial Neural Networks, LNCS 4668, pp. 370-379, 2007
  88. A. Zhou, Q. Zhang, Y. Jin, B. Sendhoff. Adaptive modeling strategy for continuous multi-objective optimization. Congress on Evolutionary Computation, pp.431-437, September 2007
  89. A. Zhou, Q. Zhang, Y. Jin, B. Sendhoff, E. Tsang, Global multi-objective optimization via estimation of distribution with biased initialization and crossover. Genetic and Evolutionary Computation Conference, pp.617—623, July 8-11, 2007
  90. D. Lim, Y.-S. Ong, Y. Jin, B. Sendhoff. A study on meta-modeling techniques, ensembles and multi-surrogates in evolutionary computation. Genetic and Evolutionary Computation Conference, pp.1288-1295, July 8-11, 2007   
  91. I. Paenke, J. Branke, and Y. Jin. On the influence of phenotype plasticity on genotype diversity. 2007 IEEE Symposium on Foundations of Computational Intelligence, pp.33-40, April 1-4, 2007, Honolulu, Hawaii, 2007 (Best student paper)
  92. A. Zhou, Y. Jin, Q. Zhang, B. Sendhoff, E. Tsang. Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization. The Fourth International Conference on Evolutionary Multi-Criterion Optimization. Pp. 832-846, LNCS 4403, Springer, 2007
  93. A. Zhou, Q. Zhang, Y. Jin, B. Sendhoff, E. Tsang. Modeling the population distribution in multi-objective optimization by generative topographic mapping. Parallel Problem Solving from Nature, LNCS 4193, pp.443-452, 2006
  94. D. Lim, Y.-S. Ong, Y. Jin, B. Sendhoff. Trusted evolutionary algorithms. Congress on Evolutionary Computation, pp.456-463, 2006
  95. L. Gräning, Y. Jin, B. Sendhoff. Generalization improvement in multi-objective learning. Int. Joint Conference in Neural Networks, pp.9893-9900, 2006
  96. Y. Jin, B. Sendhoff. Alleviating catastrophic forgetting via multi-objective learning. International Conference on Neural Networks, pp.6367-6374, 2006
  97. A. Zhou, Y. Jin, Q. Zhang, B. Sendhoff, E. Tsang. Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion. Congress on Evolutionary Computation, pp.3234-3240, 2006
  98. A. Zhou, Q. Zhang, Y. Jin, E. Tsang, T. Okabe. A model-based evolutionary algorithm for bi-objective optimization. Congress on Evolutionary Computation, pp.2568-2575, Edinburgh, September 2005
  99. V. Khare, X. Yao, B. Sendhoff, Y. Jin, and H. Wersing. Co-evolutionary modular neural networks for automatic problem decomposition. Congress on Evolutionary Computation, pp.2691-2698, Edinburgh, September 2005
  100. T. Okabe, Y. Jin, and B. Sendhoff. Theoretical comparisons of search dynamics of genetic algorithms and evolution strategies. Congress on Evolutionary Computation, pp.382-389, Edinburgh, September 2005
  101. T. Okabe, Y. Jin, and B. Sendhoff. A new approach to dynamics analysis of genetic algorithms without selection. Congress on Evolutionary Computation, pp.374-381, Edinburgh, September 2005
  102. Y. Jin, M. Olhofer, and B. Sendhoff. On evolutionary optimization of large problems with small populations. Int. Conf. on Natural Computation. LNCS 3611,  pp.1145-1154, Springer, Changsha, China
  103. L. Gräning, Y. Jin, B. Sendhoff. Efficient evolutionary optimization using individual-based evolution control and neural networks: A comparative study. European Symposium on Artificial Neural Networks. pp.273-278, Bruges, April 2005
  104. Y. Jin, B. Sendhoff, and E. 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
  105. T. Okabe, Y. Jin, M. Olhofer, and B. Sendhoff. On test functions for evolutionary multi-objective optimization. Parallel Problem Solving from Nature, VIII, LNCS 3242, Springer, pp.792-802, September 2004
  106. T. Okabe, Y. Jin, B. Sendhoff and M. Olhofer. Voronoi-based estimation of distribution algorithm for multi-objective optimization. Congress Evolutionary Computation, pp. 1594-1602, Portland, 2004
  107. Y. Jin, T. Okabe and B. Sendhoff. Neural network regularization and ensembling using multi-objective evolutionary algorithms. Congress on Evolutionary Computation, pp.1-8, Portland, 2004
  108. Y. Jin and B. Sendhoff. Reducing fitness evaluations using clustering techniques and neural network ensembles. Genetic and Evolutionary Computation Conference. LNCS 3102, Springer, pp. 688-699, Seattle, 2004
  109. Y. Jin and B. Sendhoff. Constructing dynamic test problems using the multi-objective optimization concept. In: Applications of Evolutionary Computing. LNCS 3005, pp.525-536, Springer, 2004
  110. Y. Jin and B. Sendhoff. Connectedness, regularity and the success of local search in evolutionary multi-objective optimization. In: Congress on Evolutionary Computation, Vol.3, pp.1910-1917, 2003
  111. L. Willmes, Th. Bäck, Y. Jin and B. Sendhoff. Comparing neural networks and kriging in fitness approximation in evolutionary optimization. Congress on Evolutionary Computation, Vol.1, pp.663-670, 2003
  112. T. Okabe, Y. Jin and B. Sendhoff. A critical survey of performance indices for multi-objective optimization. Congress on Evolutionary Computation, pp.878-885, 2003
  113. T. Okabe, K. Foli, M. Olhofer, Y. Jin and B. Sendhoff. Comparative studies on micro heat exchanger optimization. In: Congress on Evolutionary Computation, Vol.1, pp.647-654, 2003
  114. T. Okabe, Y. Jin and B. Sendhoff. Evolutionary multi-objective optimization with a hybrid representation. In: Congress on Evolutionary Computation, Vol.4, pp. 2262-2269, 2003
  115. Y. Jin, T. Okabe and B. Sendhoff. Solving three-objective optimization problems using evolutionary dynamic weighted aggregation: Results and analysis. In: Genetic and Evolutionary Computation Conference, pp.636, Chicago, 2003
  116. Y. Jin and B. Sendhoff. Trade-off between performance and robustness: An evolutionary multi-objective approach. In: The Second International Conference on Evolutionary Multi-criteria Optimization. LNCS 2632, Springer, pp.237-251, Faro, 2003
  117. Y. Jin and B. 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
  118. T. Okabe, Y. Jin, B. Sendhoff. On the dynamics of multi-objective optimization. In: Genetic and Evolutionary Computation Conference, pp. 247-256, New York, July 2002 (Best paper nomination)
  119. Y. Jin. Fitness approximation in evolutionary computation - A survey. In: Genetic and Evolutionary Computation Conference, pp.1105-1112, New York, July 2002
  120. Y. Jin and B. Sendhoff. Incorporation of fuzzy preferences into evolutionary multiobjective optimization. In: Proceedings of Genetic and Evolutionary Computation Conference, pp.683, New York, July 2002
  121. Y. Jin, M. Olhofer and B. Sendhoff. Managing approximate models in evolutionary aerodynamic design optimization. In: Congress on Evolutionary Computation, vol.1, pp.592-599. Seoul, Korea, May 2001
  122. M. Olhofer, Y. Jin and B. Sendhoff. Adaptive encoding for aerodynamic shape optimization using evolution strategies. In: Congress on Evolutionary Computation, vol.1, pp.576-583, Seoul, Korea, May 2001
  123. Y. Jin, M. Olhofer and B. Sendhoff. Dynamic weighted aggregation for evolutionary multi-objective optimization: Why does it work and how? In: Genetic and Evolutionary Computation Conference, pp.1042-1049, San Francisco, USA, 2001
  124. Y. Jin, T. Okabe and B. Sendhoff. Adapting weighted aggregation for multi-objective evolution strategies. In: The First International Conference on Evolutionary Multi-criterion Optimization. LNCS 1993, Springer, pp.96-110, Zurich, Switzerland, March 7-9, 2001
  125. Y. Jin, M. Olhofer and B. Sendhoff. On evolutionary optimization with approximate fitness functions. In: The Genetic and Evolutionary Computation Conference, Las Vegas, Nevada, USA. pp.786- 793, July 10-12, 2000
  126. A. Buczak, Y. Jin, H. Darabi and M. Jafari. Genetic algorithm based sensor network optimization for target tracking. Intelligent Engineering Systems through Artificial Neural Networks, Vol. 9, pp.349-354, 1999
  127. R. Burne, A. Buczak, Y. Jin, V. Jamalabad, I. Kadar and E. Eadan. A self-organizing, cooperative sensor network for remote surveillance: Current results. SPIE Proceedings of Unattended Ground Sensor Technologies and Applications, Vol.3713, pp.238-248, 1999
  128. Y. Jin, W. von Seelen and B. Sendhoff. An approach to rule-based knowledge extraction. In: IEEE International Conference on Fuzzy Systems, Anchorage, Alaska, pp.1188-1193, 1998
  129. Y. Jin, J. Zhu and J. Jiang. Fuzzy linearization of nonlinear systems. In: IEEE International Conference on Fuzzy Systems, pp.1688-1672, Orlando, Florida, USA, 1994

Refereed Journal Papers (in Chinese)

  1. Y. Jin, J.P. Jiang. Performance analysis of fuzzy controllers based on genetic algorithms. Pattern Recognition and Artificial Intelligence 10(1):75-80, 1997 (in Chinese)
  2. Y. Jin, Jingping Jiang. Two approaches to fuzzy optimal control. Proceedings of Chinese Society of Electrical Engineering. 16(3):201-204, 1996 (in Chinese)
  3. Y. Jin and J.P. Jiang. Optimization of fuzzy control rules by means of genetic algorithms. Control and Decision, 11(6):672-676, 1996 (in Chinese)
  4. Y. Jin, J. Zhu. Neural network based fuzzy modeling and its simulation techniques. Journal of Systems Simulation, 7(2):46-55, 1995 (in Chinese)
  5. Y. Jin, J.P. Jiang. Neuro-fuzzy control of robot manipulators. Chinese Journal of Robot. 17(3):157-163, 1995 (in Chinese)
  6. Y. Jin, J.P. Jiang. A neural network model with applications. Journal of Zhejiang University, 29(3):340-347, 1995 (in Chinese)
  7. Y. Jin, J.P. Jiang. Fuzzy logic integrated multivariable adaptive neuro-control. Information and Control, 23(4):223-228, 1994 (in Chinese)
  8. Y. Jin, J. Zhu. Neural network based self-learning fuzzy control. Chinese Journal of Electronics Technology, 4:35-40, 1994 (in Chinese)
  9. Y. Jin, J. Zhu and J.P. Jiang. State estimation and adaptive control of multivariable systems using fuzzy logic and neural networks. AMSE Advances in Modeling and Analysis, 43(2), 1994
  10. Y. Jin, X. Shen. Two-level hierarchical intelligent fuzzy control of servo systems. Journal of Zhejiang University, 28(6):644-654, 1994 (in Chinese)
  11. Y. Jin, J.P. Jiang. Adaptive fuzzy prediction with application to weather forecast. Pattern Recognition and Artificial Intelligence, 6(4):283-290, 1993 (in Chinese)
  12. Y. Jin, J.P. Jiang. Neural network based non-linear feedback control. Journal of Zhejiang University, 27, 1993 (in Chinese)
  13. Y. Jin, J.P. Jiang. Fuzzy logic integrated variable structure control of a class of nonlinear systems. Control and Decision, 7(1):36-40, 1992 (in Chinese)

Edited Books / Conference Proceedings

  1. H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, and C. Grimme. (Eds.). 9th International Conference Evolutionary Multi-Criterion Optimization (EMO 2017), Münster, Germany, March 19-22, 2017
  2. Y. Jin and S. Kollias (Editors). 2016 IEEE Symposium Series on Computational Intelligence, Athens, Greece, 2016
  3. Y. Meng and Y. Jin (Editors). Bio-Inspired Self-Organization of Robotic Systems. Springer, 2011
  4. Y. Jin and L. Wang (Editors). Fuzzy Systems in Bioinformatics and Computational Biology. Springer, Berlin Heidelberg, 2009
  5. S. Yang, Y.S. Ong, and Y. Jin (Editors). Evolutionary Computation in Dynamic and Uncertain Environments. Springer, Berlin Heidelberg, 2007
  6. Y. Jin (editor). Multi-Objective Machine Learning. Springer, Berlin Heidelberg. 2006
  7. L. Wang and Y. Jin (editors), 2005 International Conference on Fuzzy Systems and Knowledge Discovery. LNAI 3613, 3614, Springer, August 2005
  8. F. Rothlauf, J. Branke, S. Codnoni, D.W. Corne, R. Drechsler, Y. Jin, P. Machado, E. Marchiori, J. Romerero, G.D. Smith,  G. Squillero (editors). Applications of Evolutionary Computing. LNCS 3449, Springer, March 2005
  9. Y. Jin (editor). Knowledge Incorporation in Evolutionary Computation. Springer, 2005
  10. G. Raidl, S. Cagnoni, J. Branke, D.W. Corne, R. Drechsler, Y. Jin, C.G. Johnson, P. Machado,  E. Marchiori, F. Rothlauf, G.D. Smith, G. Squillero (editors). Applications of Evolutionary Computing. LNCS 3005, Springer, April 2004

Invited / Contributed Book Chapters

  1. H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, and C. Grimme. (Eds.). 9th International Conference Evolutionary Multi-Criterion Optimization (EMO 2017), Münster, Germany, March 19-22, 2017
  2. Y. Jin and S. Kollias (Editors). 2016 IEEE Symposium Series on Computational Intelligence, Athens, Greece, 2016
  3. Y. Meng and Y. Jin (Editors). Bio-Inspired Self-Organization of Robotic Systems. Springer, 2011
  4. Y. Jin and L. Wang (Editors). Fuzzy Systems in Bioinformatics and Computational Biology. Springer, Berlin Heidelberg, 2009
  5. S. Yang, Y.S. Ong, and Y. Jin (Editors). Evolutionary Computation in Dynamic and Uncertain Environments. Springer, Berlin Heidelberg, 2007
  6. Y. Jin (editor). Multi-Objective Machine Learning. Springer, Berlin Heidelberg. 2006
  7. L. Wang and Y. Jin (editors), 2005 International Conference on Fuzzy Systems and Knowledge Discovery. LNAI 3613, 3614, Springer, August 2005
  8. F. Rothlauf, J. Branke, S. Codnoni, D.W. Corne, R. Drechsler, Y. Jin, P. Machado, E. Marchiori, J. Romerero, G.D. Smith,  G. Squillero (editors). Applications of Evolutionary Computing. LNCS 3449, Springer, March 2005
  9. Y. Jin (editor). Knowledge Incorporation in Evolutionary Computation. Springer, 2005
  10. G. Raidl, S. Cagnoni, J. Branke, D.W. Corne, R. Drechsler, Y. Jin, C.G. Johnson, P. Machado,  E. Marchiori, F. Rothlauf, G.D. Smith, G. Squillero (editors). Applications of Evolutionary Computing. LNCS 3005, Springer, April 2004

Granted Patents

  • Combining model-based and genetics-based offspring generation for mulit-objective optimization using a convergence criterion, US Patent No 7739206, 2010
  • Fuzzy preferences in multi-objective optimization (MOO), US Patent No 7383236, 2008 / Japan Patent No 433510, 2009
  • Estimation of distribution algorithm (EDA). US Patent No 7428514, 2008
  • Multi-objective optimization, US Patent No 7363280, 2008
  • Reduction of fitness evaluations using clustering techniques and neural network ensembles. European Patent No 1557788 / US Patent No 7363281, 2008
  • Approximate fitness functions. US Patent No 7043462, 2006