JOURNAL PAPERS

ARTIFICIAL INTELLIGENCE - MACHINE LEARNING & APPLICATIONS

  1. N. Lu, S. Liu, R. He, Q. Wang, Y. S. Ong and Ke Tang, "Large Language Models can be Guided to Evade AI-generated Text Detection", Transactions on Machine Learning Research, In Press, 2024.

  2. T. He, Y. Liu, Y. S. Ong, Xiaohu Wu and X. Luo, "Polarized Message-Passing in Graph Neural Networks", Artificial Intelligence, In Press, 2024.

  3. J. Dong, Y. Wang, X. Xie, J. Lai and Y. S. Ong, "Generalizable and Discriminative Representations for Adversarially Robust Few-Shot Learning", IEEE Transactions on Neural Networks and Learning Systems, In Press, 2024.

  4. H. X. Choong, Y. S. Ong, A. Gupta, C. Chen and R. Lim, "Jack and Masters of All Trades: One-pass Learning Sets of Model Sets from Large Pre-trained Models", IEEE Computational Intelligence Magazine, Vol. 18, No. 3, pps. 29-40, 2023.

  5. Y. Hou, M. Sun, Y. Zeng, Y. S. Ong, Y. Jin, H. Ge, and Q. Zhang,"A Multi-agent Cooperative Learning System with Evolution of Social Roles", IEEE Transactions on Evolutionary Computation, doi: 10.1109/TEVC.2023.3268076, In Press, 2023.

  6. C. S. Tan, A. Gupta, Y. S. Ong , M. Pratama, P. S. Tan and S. K. Lam, "Pareto optimization with small data by learning across common objective spaces", Scientific Reports, Vol. 13, No. 7842, 2023.

  7. Y. Xie, Y. Liang, M. Gong, A. K. Qin, T. He and Y. S. Ong, "Semisupervised Graph Neural Networks for Graph Classification", IEEE Transactions on Cybernetics, Vol. 53, No. 10, pps. 6222-6235, 2023.

  8. M. Gong, Y. Gao, Y. Wu, Y. Zhang, A. K. Qin and Y. S. Ong, "Heterogeneous Multi-party Learning with Data-driven Network Sampling", IEEE Transactions on Pattern Analysis and Machine Intelligence, In Press, 2023.

  9. Y. Gao, M. Gong, Y. S. Ong, A. K. Qin, Y. Wu and F. Xie, "A Collaborative Multimodal Learning-Based Framework for COVID-19", IEEE Transactions on Neural Networks and Learning Systems, In Press, 2023.

  10. Z. Chen, M. Wu, A. Chan, X. Li and Y. S. Ong, "A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges", IEEE Computational Intelligence Magazine, Vol. 18, No. 2, pps. 60 - 77, 2023.

  11. Suryadi, L. Y. Chew and Y. S. Ong, "Granger Causality Using Jacobian in Neural Networks", Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 33, No. 2, 2023.

  12. P. Wei, Y. Ke, Y. S. Ong Z. Ma, "Adaptive Transfer Kernel Learning for Transfer Gaussian Process Regression", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, No. 6, pps. 7142 - 7156, 2023.

  13. Z. Chen, A. Gupta, L. Zhou, and Y. S. Ong, "Scaling Multiobjective Evolution to Large Data with Minions: A Bayes-Informed Multitask Approach", IEEE Transactions on Cybernetics, In Press, 2022.

  14. J. C. Wong, A. Gupta, C. C. Ooi and Y. S. Ong, "Learning in Sinusoidal Spaces with Physics-Informed Neural Networks", IEEE Transactions on Artificial Intelligence, In Press, 2022.

  15. A. Gupta, L. Zhou, Y. S. Ong, Z. Chen and Y. Hou, "Half a Dozen Real-World Applications of Evolutionary Multitasking, and More", IEEE Computational Intelligence Magazine, Vol. 17, No. 2, pps. 49 - 66, 2022.

  16. P. Wei , T. V. Vo, X. Qu, Y. S. Ong and Z. Ma, "Transfer Kernel Learning for Multi-Source Transfer Gaussian Process Regression", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, No. 3, pps. 3862 - 3876, 2023.

  17. H. Liu, K. Wu, Y. S. Ong, C. Bian, X. Jiang and X. Wang, "Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains", IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 53, No. 10, pps. 6232-6244, 2023. Available here as PDF file.

  18. H. Zhao, J.T. Zhou and Y. S. Ong, "Word2Pix: Word to Pixel Cross Attention Transformer in Visual Grounding", IEEE Transactions on Neural Networks and Learning Systems, In Press, 2022.

  19. Z. Guo, Y. S. Ong, T. He and H. Liu, "Co-Learning Bayesian Optimization", IEEE Transactions on Cybernetics, Vol. 52, No. 9, pps. 9820-9833, 2022.

  20. Z. Wang, Q. Zhang; Y. S. Ong, S. Yao; H. Liu and J. Luo, "Choose Appropriate Subproblems for Collaborative Modeling in Expensive Multiobjective Optimization", IEEE Transactions on Cybernetics, Vol. 53, No. 1, pps. 483 - 496, 2023.

  21. Z. Guo, H. Liu, X. Qu, Y. S. Ong, Y. Zhang and J. Zheng, "Generative Multi-Form Bayesian Optimization", IEEE Transactions on Cybernetics, Vol. 53, No. 7, pps. 4347 - 4360, 2023.

  22. M. Shakeri, E. Miahi, A. Gupta and Y. S. Ong, "Scalable Transfer Evolutionary Optimization: Coping with Big Task-Instances", IEEE Transactions on Cybernetics, Vol. 53, No. 10, pps. 6160-6172, 2023.

  23. J. Dong, B. Hou, L. Feng, H. Tang, K. C. Tan and Y. S. Ong, "A Cell-Based Fast Memetic Algorithm for Automated Convolutional Neural Architecture Design", IEEE Transactions on Neural Networks and Learning Systems, In Press, 2022.

  24. Y. Gao, M. Gong, Y. Xie, A. K. Qin, K. Pan and Y. S. Ong, "Multiparty Dual Learning", IEEE Transactions on Cybernetics, Vol. 53, No. 5, pps. 2955 - 2968, 2023.

  25. X. Qu, Y. S. Ong, A. Gupta and Z. Sun, "Adversary Agnostic Robust Deep Reinforcement Learning", IEEE Transactions on Neural Networks and Learning Systems, Vol. 34, No. 9, pps. 6146 - 6157, 2023.

  26. L. Feng, Q. Shang, Y. Hou, K. C. Tan and and Y. S. Ong, "Multi-Space Evolutionary Search for Large-Scale Optimization with Applications to Recommender Systems", IEEE Transactions on Artificial Intelligence, Vol. 4, No. 1, pps. 107 - 120, 2023.

  27. N. Zhang, Z. Chen, A. Gupta and Y. S. Ong, "Evolutionary Machine Learning with Minions: A Case Study in Feature Selection", IEEE Transactions on Evolutionary Computation, Vol. 26, No. 1, pps. 130-144, 2022.

  28. A. Chan, L. Ma, J. Xu, Y. S. Ong, X. Xie, M. H. Xue and Y. Liu, "Breaking Neural Reasoning Architectures with Metamorphic Relation-Based Adversarial Examples", IEEE Transactions on Neural Networks, Vol. 33, No. 11, pps. 6976 - 6982, 2022.

  29. H. Liu, Y. S. Ong, X. Jiang and X. Wang, "Deep Latent-Variable Kernel Learning", IEEE Transactions on Cybernetics, Vol. 52, No. 10, 2022. Available here as PDF file.

  30. T. T. He, L. Bai and Y. S. Ong, "Vicinal Vertex Allocation for Matrix Factorization in Networks", IEEE Transactions on Cybernetics, Vol. 52, No. 8, pps. 8047 - 8060, 2022.

  31. L. Bai, W. Lin, A. Gupta and Y. S. Ong, "From Multi-Task Gradient Descent to Gradient-Free Evolutionary Multitasking: A Proof of Faster Convergence", IEEE Transactions on Cybernetics, Vol. 52, No. 8, pps. 8561 - 8573, 2022. Available here as PDF file.

  32. X. Qu, Y. S. Ong and A. Gupta, "Frame-Correlation Transfers Trigger Economical Attacks on Deep Reinforcement Learning Policies", IEEE Transactions on Cybernetics, Vol. 52, No. 8, pps. 7577 - 7590, 2022. Available Here

  33. L. Feng, Y. Huang, I. W. Tsang, A. Gupta, K. Tang, K. C. Tan and Y. S. Ong, "Towards Faster Vehicle Routing by Transferring Knowledge From Customer Representation", IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 2, pps. 952 - 965, 2022.

  34. P. Wei, R. Sagarna, Y. Ke and Y. S. Ong, "Easy-but-effective Domain Sub-similarity Learning for Transfer Regression", IEEE Transactions on Knowledge and Data Engineering, Vol. 34, No. 9, pps. 4161 - 4171, 2022.

  35. J. C. Wong, A. Gupta and Y. S. Ong, "Can Transfer Neuroevolution Tractably Solve Your Differential Equations?", IEEE Computational Intelligence Magazine, Vol. 16, No. 2, pps. 14 - 30, 2021. Available here as PDF file. Source code available here tNES.

  36. P. Wei, R. Sagarna, Y. Ke and Y. S. Ong, "Practical Multi-Source Transfer Regression with Source-Target Similarity Captures", IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 8, pps. 3498-3509, 2021.

  37. Z. Guo, Y. S. Ong and H. Liu, "Calibrated and Recalibrated Expected Improvements for Bayesian Optimization", Structural and Multidisciplinary Optimization, Vol. 64, pps. 3549-3567, 2021.

  38. T. T. He, Y. S. Ong, P. Hu, "Multi-Source Propagation Aware Network Clustering", Neurocomputing, vol. 453, pps. 119-130, 2021.

  39. H. Liu, Y. S. Ong, X. Jiang and X. Wang, "Modulating Scalable Gaussian Processes for Expressive Statistical Learning", Pattern Recognition, Vol. 120, 2021. Available here as PDF file.

  40. R. Lim, L. Zhou, A. Gupta, Y. S. Ong and A. N. Zhang, "Solution Representation Learning in Multi-Objective Transfer Evolutionary Optimization", Vol. 9, pps. 41844-41860, IEEE Access, 2021.

  41. H. Liu, Y. S. Ong, and J. F. Cai, "Large-scale Heteroscedastic Regression via Gaussian Process", IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No.1, pps. 708-721, 2021. Available here as PDF file

  42. Z. Zhang, Y. S. Ong, D. Q. Wang and B. Q. Xue "A Collaborative Multi-Agent Reinforcement Learning Method Based on Policy Gradient Potential", IEEE Transactions on Cybernetics, Vol. 51, No. 2, pps. 1015-1027, 2021.

  43. Y. Hou, Y. S. Ong, J. Tang and Y. Zeng, "Evolutionary Multi-Agent Transfer Learning with Model-based Opponent Behavior Prediction", IEEE Transactions on Cybernetics, Vol. 51, No. 10, pps. 5962-5976, 2021.

  44. W. M. Tan, A. Gupta and Y. S. Ong, "Generalizing Transfer Bayesian Optimization to Source-Target Heterogeneity", IEEE Transactions on Automation Science and Engineering, Vol. 18, No. 4, pps. 1754-1765, 2021.

  45. H. Liu, Y. S. Ong, X. Shen, and J. F. Cai, "When Gaussian Process Meets Big Data: A Review of Scalable GPs", IEEE Transactions on Neural Networks and Learning Systems, Vol. 31, No. 11, 2020. Available here as PDF file

  46. L. Bai, Y. S. Ong, T. He and A. Gupta, "Multi-task Gradient Descent for Multi-task Learning", Memetic Computing, Vol. 12, pps. 355-369, 2020.

  47. X. Qu, Z. Sun, Y. S. Ong, P. Wei and A. Gupta, "Minimalistic Attacks: How Little it Takes to Fool Deep Reinforcement Learning Policies", IEEE Transactions on Cognitive and Developmental Systems, Vol. 13, No. 4, pps. 806-817, 2021.

  48. W. L. Ng, A. Chan, Y. S. Ong and C. K. Chua, "Deep learning for fabrication and maturation of 3D bioprinted tissues and organs", Virtual and Physical Prototyping, Vol. 15, No. 3, pps. 340-358, 2020.

  49. X. Han, T. He, Y. S. Ong and Y. Zhong, "Precise object detection using adversarially augmented local/global feature fusion", Engineering Applications of Artificial Intelligence, Vol. 94, 2020.

  50. A. Ashfahani, M. Pratama, E. Lughofer and Y. S. Ong, "DEVDAN: Deep evolving denoising autoencoder", Neurocomputing, No. 390, pps. 297-314, 2020.

  51. T. He, Y. Liu, T. H. Ko, K. C. C. Chan and Y. S. Ong, "Contextual Correlation Preserving Multi-View Featured Graph Clustering", IEEE Transactions on Cybernetics, Vol. 50, No. 10, pps. 4318-4331, 2020.

  52. D. Xu, Y. Shi, Ivor W. Tsang, Y. S. Ong, C. Gong, and X. Shen, "A Survey on Multi-output Learning", IEEE Transactions on Neural Networks and Learning Systems, In Press 2019.

  53. Y. S. Ong and A. Gupta, "AIR5: Five Pillars of Artificial Intelligence Research", IEEE Transactions on Emerging Topics in Computational Intelligence, pps. 411-415, Vol. 3, No. 5, 2019. Available here as PDF file

  54. W. Guo, Y. S. Ong, Y. Zhou, J. R. Hervas, A. Song and H. Wei, "Fisher Information Matrix of Unipolar Activation Function-Based Multilayer Perceptrons", IEEE Transactions on Cybernetics, vol. 49, no. 8, pps. 3088 - 3098, 2019.

  55. W. Y. Deng, A. Lendasse, Y. S. Ong, I. Tsang, L. C. Lin and Q. H. Zheng, "Domain Adaptation via Feature Selection on Explicit Feature Map", IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, No. 4, pps. 1180-1190, 2019. Available here as PDF file.

  56. B. Da, Y. S. Ong, A. Gupta, L. Feng and H. Liu, "Fast Transfer Gaussian Process Regression with Large-Scale Sources", Knowledge-Based Systems, Vol. 165, pps. 208-218, 2019. Available here as PDF file.

  57. B. Da, A. Gupta, Y. S. Ong, "Curbing Negative Influences Online for Seamless Transfer Evolutionary Optimization", IEEE Transactions on Cybernetics, Vol. 49, No. 12, pps. 4365-4378, 2019. Paper available here as PDF file. Source code available at Github.

  58. H. Hu, Y. Luo, Y. Wen, Y. S. Ong and X. Zhang, "How to Find a Perfect Data Scientist: A Distance-Metric Learning Approach", IEEE Access, Vol. 6, No.1, pps. 60380-60395, Dec 2018.

  59. H. Liu, J. F. Cai, Y. S. Ong and Y. Wang, "Understanding and Comparing Scalable Gaussian Process Regression for Big Data", Knowledge-Based Systems, vol. 164, pps. 324-335, 2018.

  60. W. Guo, H. Wei, Y. S. Ong, J. R. Hervas, J. Zhao, H. Wang, K. Zhang and Q. H. Zheng, "Numerical Analysis near Singularities in RBF Networks", Journal of Machine Learning Research, vol. 19, no. 1, pps.1-39, 2018.

  61. J. R. Hervas, A. Gupta and Y. S. Ong, "Data-driven Risk Assessment and Multicriteria Optimization of UAV Operations", Aerospace Science and Technology, vol. 77, pps 510-523, 2018.

  62. H. Liu, J. F. Cai and Y. S. Ong, "Remarks on Multi-Output Gaussian Process Regression", Knowledge-Based Systems, vol. 144, pps. 102-121, 2018. Available here as PDF file.

  63. A. Gupta, Y. S. Ong and L. Feng, "Insights on Transfer Optimization: Because Experience is the Best Teacher", IEEE Transactions on Emerging Topics in Computational Intelligence, Vol.2, No. 1, pps. 51 - 64, 2018. Available here as PDF file.

  64. X. Shen, W. Liu, I. Tsang, Q. S. Sun and Y. S. Ong, "Multilabel Prediction via Cross-view Search", IEEE Transactions on Neural Networks and Learning Systems, vol.29, no.9, pps. 4324 - 4338, 2017.

  65. H. Liu, Y. S. Ong, J. F. Cai and Y. Wang, "Cope with Diverse Data Structures in Multi-fidelity Modeling: A Gaussian Process Method", Engineering Applications of Artificial Intelligence, vol. 67, pps. 211-225, 2018. Available here as PDF file.

  66. H. Liu, J. R. Hervas, Y. S. Ong, J. F. Cai and Y. Wang, "An adaptive RBF-HDMR modeling approach under limited computational budget", Structural and Multidisciplinary Optimization, vol. 57, pps. 1233-1250, 2018. Available here as PDF file.

  67. H. Liu, Y. S. Ong and J. F. Cai, "A Survey of Adaptive Sampling for Global Metamodeling in support of Simulation-based Complex Engineering Design", Structural and Multidisciplinary Optimization, vol. 57, pps. 393-416, 2018. Available here as PDF file.

  68. H. Liu, J. F. Cai and Y. S. Ong, "An Adaptive Sampling Approach for Kriging Metamodeling by Maximizing Expected Prediction Error", Computers and Chemical Engineering, No. 106, pps. 171-182, 2017. Available here as PDF file.

  69. Y. Zhai, Y. S. Ong and I. W. Tsang, "Making Trillion Correlations Feasible in Feature Grouping and Selection", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, No. 12, pp. 2472-2486, 2016. Available here as PDF file.

  70. G. Kim, Y. S. Ong, T. Cheong and P. S. Tan, "Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion", IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 8, pps. 2367 - 2380, 2016. Available here as PDF file.

  71. M. Swiechowski, J. Mandziuk and Y. S. Ong, "Specialization of a UCT-based General Game Playing Program to Single-Player Games", IEEE Transactions on Computational Intelligence and AI in Games, Vol. 8, No. 3, pps. 218-228, 2016.

  72. I. Chaturvedi, Y. S. Ong, I. W. Tsang, RE Welsch and E Cambria, "Learning Word Dependencies in Text by Means of a Deep Recurrent Belief Network", Knowledge-Based Systems, Vol. 108, pps. 144-154, 2016.

  73. I. Chaturvedi, Y. S. Ong and R. V. Arumugam, "Deep Transfer Learning for Classification of Time-Delayed Gaussian Networks", Signal Processing, Vol. 110, pps. 250-262, 2015.

  74. Y. Zhai, Y. S. Ong and I. W. Tsang, "The Emerging Big Dimensionality", IEEE Computational Intelligence Magazine, Vol. 9, No. 3, pp. 14-26, 2014. Available here as PDF file.

  75. T.-J. Hsieh, Y. S. Ong, C. P. Su and C.-W. Seah, "Trend Mining for System Reliability Employing a Novel Heuristic-based Kriging Profiling Method", Applied Mathematical Modelling, Vol. 38, No 19-20, 2014.

  76. C. W. Seah, I. W. Tsang and Y. S. Ong, "Transfer Ordinal Label Learning", IEEE Transactions on Neural Networks and Learning Systems, Vol. 24, No. 11, pps. 1863-1876, 2013. Available here as PDF file.

  77. C. W. Seah, Y. S. Ong, and I. W. Tsang, "Combating Negative Transfer from Predictive Distribution Differences", IEEE Transactions On Cybernetics, Vol. 43, No. 4, pps. 1153-1165, 2013. Available here as PDF file.

  78. E. M. Kan, M. H. Lim, Y. S. Ong, A. H. Tan and S. P. Yeo, "Extreme learning machine terrain-based navigation for unmanned aerial vehicles", Neural Computing & Applications, pp. 1-9, 2012. Available here as PDF file.

  79. C. W. Seah, I. W. Tsang and Y. S. Ong, "Transductive Ordinal Regression", IEEE Transactions on Neural Networks and Learning Systems, Vol. 23, No. 7, pps. 1074-1086, 2012. Available here as PDF file.

  80. A-H. Tan, Y. S. Ong and A. Tapanuj, "A Hybrid Agent Architecture Integrating Desire, Intention and Reinforcement Learning", Expert Systems with Applications, Vol. 38, No. 7, pp. 8477-8487, 2011.

  81. H. J. Rong, Y. S. Ong, A. H. Tan and Z. Zhu, "A fast pruned-extreme learning machine for classification problem", Neurocomputing, Vol. 72, No. 1-3, pp. 359-366, December 2008. Available here.

  82. K. T. Seow, K. M. Sim, Y. S. Ong, E. P. Sulaiman, "A BDI Assignment Protocol with New Cooperative-Concession Strategies", IEEE Transactions on Systems, Man, and Cybernetics Part A, Vol. 38, No. 3, 2008.

  83. Q. Cao, M. H. Lim, J. H. Li, Y. S. Ong, W. L. Ng, "A Context Switchable Fuzzy Inference Chip", IEEE Transactions on Fuzzy Systems, Vol. 14, No. 4, pp. 552-567, August 2006.

  84. C. W. Yeu , M. H. Lim, G. B. Huang, A. Agarwal and Y. S. Ong, "A New Machine Learning Paradigm for Terrain Reconstruction", IEEE Geoscience and Remote Sensing Letters, Vol. 3, No. 3, pp. 382-386, July 2006.

  85. Y. S. Ong and A.J. Keane, "A domain knowledge based search advisor for design problem solving environments", Engineering Applications of Artificial Intelligence, 2002, Vol. 15, No. 1, pp. 105-116. Available here as PDF file.



EVOLUTIONARY & MEMETIC COMPUTATION (Theory, Algorithm, Survey & Applications)

  1. J. Yuan, H.-L. Liu, Y. S. Ong and Z. He, "Indicator-based Evolutionary Algorithm for Solving Constrained Multi-objective Optimization Problems", IEEE Transactions on Evolutionary Computation, Vol. 26, No. 2, pps. 379-391, 2022.*Bestowed the 2024 IEEE CIS Outstanding Transactions on Evolutionary Computation Paper Award.

  2. L. Bai, W. Lin, A. Gupta and Y. S. Ong, "From Multi-Task Gradient Descent to Gradient-Free Evolutionary Multitasking: A Proof of Faster Convergence", IEEE Transactions on Cybernetics, Vol. 52, No. 8, pps. 8561-8573, 2022. Available here as PDF file.

  3. L. Zhou, L. Feng, A. Gupta. and Y. S. Ong, "Learnable Evolutionary Search across Heterogeneous Problems via Kernelized Autoencoding", IEEE Transactions on Evolutionary Computation, Vol. 25, No. 3, pps. 567-581, 2021. Available here as PDF file.

  4. K. K. Bali, A. Gupta,Y. S. Ong and P. S. Tan, "Cognizant Multitasking in Multi-Objective Multifactorial Evolution: MO-MFEA-II", IEEE Transactions on Evolutionary Computation, Vol. 51, No. 4, 2021. Available here as PDF file.

  5. Wang, R., Zeng, S., Li, C., Yang, S. and Y. S. Ong, "Handling constrained many-objective optimization problems via problem transformation", IEEE Transactions on Cybernetics, vol. 51, no. 10, pps. 4834-4847, 2021.

  6. K. K. Bali, Y. S. Ong, A. Gupta and P. S. Tan, "Multifactorial Evolutionary Algorithm with Online Transfer Parameter Estimation: MFEA-II", IEEE Transactions on Evolutionary Computation, Vol. 24, No. 1, 2020. Available here as PDF file.*Bestowed the 2023 IEEE CIS Outstanding Transactions on Evolutionary Computation Paper Award.

  7. A. Gupta, Y. S. Ong, "Back to the Roots: Multi-X Evolutionary Computation", Cognitive Computation, vol. 11, pps. 1-17, 2019. Available here: PDF file.

  8. L. Feng, A. Gupta and Y. S. Ong, "Compressed Representation for Higher-Order Meme Space Evolution: A Case Study on Big Knapsack Problems", Memetic Computing, Vol. 11, No. 1, pps. 3-17, 2019. Available here: PDF file.

  9. W. M. Tan, Y. S. Ong, A. Gupta and C. K. Goh, "MultiProblem Surrogates: Transfer Evolutionary Multiobjective Optimization of Computationally Expensive Problems", IEEE Transactions on Evolutionary Computation, Vol. 23, No. 1, 2019. Available here as PDF file.*Source code Download

  10. Z. K. Wang, Y. S. Ong and H. Ishibuchi, "On Scalable Multiobjective Test Problems with Hardly-Dominated Boundaries", IEEE Transactions on Evolutionary Computation, Vol. 23, No. 2, 2019. Available here: PDF file.

  11. H. Li, Y. S. Ong, M. Gong and Z. K. Wang, "Evolutionary Multitasking Sparse Reconstruction:Framework and Case Study", IEEE Transactions on Evolutionary Computation, , vol. 23, no. 5, pps. 733 - 747, 2018. Available here: PDF file.

  12. L. Feng, L. Zhou, J. Zhong, A. Gupta, Y. S. Ong and K. C. Tan, "Evolutionary Multitasking via Explicit Autoencoding,", IEEE Transactions on Cybernetics, vol. 49, no. 9, pps. 3457 - 3470, 2018.

  13. B. Da, A. Gupta, Y. S. Ong, "Curbing Negative Influences Online for Seamless Transfer Evolutionary Optimization", IEEE Transactions on Cybernetics, Vol. 49, No. 12, pps. 4365-4378, 2019. Paper available here as PDF file. Source code available at Github.

  14. Z. K. Wang, Y. S. Ong, J. Sun and Q. Zhang, "A Generator for Multiobjective Test Problems with Difficult to Approximate Pareto Front Boundaries", IEEE Transactions on Evolutionary Computation, vol. 23, no. 4, pps. 556 - 571, 2019.

  15. J. P. Luo, A. Gupta, Y. S. Ong and Z. K. Wang, "Evolutionary Optimization of Expensive Multi-objective Problems with Co-sub-Pareto Front Gaussian Process Surrogates", IEEE Transactions on Cybernetics, vol. 49, No. 5, 2019. Available here: PDF file.

  16. J. Zhong, L. Feng, W. T. Cai and Y. S. Ong, "Multifactorial Genetic Programming for Symbolic Regression", IEEE Transactions on Systems, Man, and Cybernetics: Systems, pps. 1-14, 2018. Available here: PDF file.

  17. Y. Yuan, Y. S. Ong, A. Gupta and H. Xu, "Objective Reduction in Many-Objective Optimization: Evolutionary Multiobjective Approach and Comprehensive Analysis", IEEE Transactions on Evolutionary Computation, Vol. 22, No. 2, pps. 89 - 210, 2018.

  18. Adam Zychowski, A. Gupta, J. Mandziuka and Y. S. Ong, "Addressing Expensive Multi-objective Games with Postponed Preference Articulation via Memetic Co-evolution", Knowledge-Based Systems, vol. 154, pps. 17-31, 2018.

  19. M. Elarbi, S. Bechikh, A. Gupta, L. B. Said and Y. S. Ong, "A New Decomposition-based NSGA-II for Many-objective Optimization", IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol.48, No. 7, pps. 1191 - 1210, 2018.

  20. A. Gupta, Y. S. Ong and L. Feng, "Insights on Transfer Optimization: Because Experience is the Best Teacher", IEEE Transactions on Emerging Topics in Computational Intelligence, Vol.2, No. 1, pps. 51 - 64, 2018. Available here: PDF file.

  21. L. Feng, Y. S. Ong, S. Jiang and A. Gupta, "Autoencoding Evolutionary Search with Learning across Heterogeneous Problems", IEEE Transactions on Evolutionary Computation, Vol. 21, No. 5, pps. 760 - 772, 2017. Available here as PDF file.

  22. J. Zhong, L. Feng and Y. S. Ong, "Gene Expression Programming: A Survey", IEEE Computational Intelligence Magazine, Vol. 12, No. 3, pps. 54-72, 2017. Available here as PDF file.

  23. M. Cheng, Y. S. Ong, A. Gupta and Z. W. Ni, "Coevolutionary Multitasking for Concurrent Global Optimization: With Case Studies in Complex Engineering Design", Engineering Applications of Artificial Intelligence, Vol. 64, pps. 13-24, 2017.

  24. Y. Hou, Y. S. Ong, L. Feng and J. M. Zurada, "Evolutionary Transfer Reinforcement Learning Framework for Multi-Agent System", IEEE Transactions on Evolutionary Computation, Vol. 21, No. 4, pps. 601-615, 2017. Available here as PDF file.

  25. Y. Zeng, X. Chen, Y. S. Ong, J. Tang and Y. Xiang, "Structured Memetic Automation for Online Human-like Social Behavior Learning", IEEE Transactions on Evolutionary Computation, Vol. 21, No. 1, pps. 102-115, 2017. Available here as PDF file.

  26. A. Gupta, C. K. Heng, Y. S. Ong, P. S. Tan and A. N. Zhang, "A Generic Framework for Multi-Criteria Decision Support in Eco-Friendly Urban Logistics Systems", Expert Systems with Applications, vol. 71, No. 1, pps. 288-300, 2017.

  27. A. Gupta, Y. S. Ong, L. Feng and K. C. Tan, "Multi-Objective Multifactorial Optimization in Evolutionary Multitasking", IEEE Transactions on Cybernetics, Vol. 47, No. 7, pps. 1652-1665, 2017. Available here as PDF file.

  28. Y. S. Ong and A. Gupta, "Evolutionary Multitasking: A Computer Science View of Cognitive Multitasking", Cognitive Computation, Vol. 8, No. 2, pps. 125-142, 2016. Available here as PDF file.

  29. A. Gupta, J. Mandziuk and Y. S. Ong, "Evolutionary Multitasking in Bi-Level Optimization", Complex & Intelligent Systems, Vol. 1, No. 1-4, pps. 83-95, 2016. Available here as PDF file.

  30. A. Gupta, Y. S. Ong and L. Feng, "Multifactorial Evolution: Towards Evolutionary Multitasking", IEEE Transactions on Evolutionary Computation, Vol. 20, No. 3, pp. 343-357, 2016. Available here as PDF file.*Source code Download*. *Bestowed the 2019 IEEE CIS Outstanding Transactions on Evolutionary Computation Paper Award.

  31. B. Da, Y. S. Ong, L. Feng, A.K. Qin, A. Gupta, Z. Zhu, C. K. Ting, K. Tang, and X. Yao, "Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results", Technical Report, 2016. Available here as PDF file


  32. Y. Yuan, Y. S. Ong, L. Feng, A.K. Qin, A. Gupta., B. Da, Q. Zhang, K. C. Tan, Y. Jin, and H. Ishibuchi, "Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results", Technical Report, 2016. Available here as PDF file.

    33. For more info on MFO, Benchmark Problems and Source Codes Downloads, Click here!

  33. L. Feng, Y. S. Ong, X. Chen and C. Chen, "Conceptual Modeling of Evolvable Local Searches in Memetic Algorithms using Linear Genetic Programming: A Case Study on Capacitated Vehicle Routing Problem", Soft Computing Journal, No. 9, 2016.

  34. E. Munoz, G. Acampora, J. M. Cadenas and Y. S. Ong, "Memetic Music Composition", IEEE Transactions on Evolutionary Computation, Vol. 20, No. 1, pps. 2202 - 2213, 2016.

  35. L. Feng, Y. S. Ong, A. H. Tan and I. W. Tsang, "Memes as Building Blocks: A Case Study on Evolutionary Optimization + Transfer Learning for Routing Problems", Memetic Computing Journal, Vol. 7, No. 3, pp. 159-180, 2015. Available here as PDF file.

  36. Y. Miche, M.-H. Lim, A. Lendasse and Y. S. Ong, "Meme representations for game agents", World Wide Web, Vol. 18, No. 2, pps. 215-234, 2015.

  37. L. Feng, Y. S. Ong, M.-H. Lim, and I. W. Tsang, "Memetic Search with Inter-Domain Learning: A Realization between CVRP and CARP", IEEE Transactions on Evolutionary Computation, Vol. 19, No. 5, pps. 644 - 658, 2015. Available here as PDF file.

  38. G. Kim, Y. S. Ong, C. K. Heng, P. S. Tan, and N. A. Zhang, "City Vehicle Routing Problem (City VRP): A Review", IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 4, pp. 1654-1666, 2015. Available here as PDF file.

  39. S. W. Jiang, J. Zhang, Y. S. Ong, A. N. S. Zhang and P. S. Tan, "A Simple and Fast Hypervolume Indicator-based Multiobjective Evolutionary Algorithm", IEEE Transactions on Cybernetics, Vol. 45, No. 10, pps. 2202 - 2213, 2015. Available here as PDF file.

  40. S. Jiang, J. Zhang and Y. S. Ong, "Multiobjective Optimization Based on Reputation", Information Science, Vol. 286, pps. 125-146, 2014.

  41. S. Jiang, Y. S. Ong, J. Zhang and L. Feng, "Consistencies or Contradictions of Performance Metrics in Multiobjective Optimization", IEEE Transactions on Cybernetics, Vol. 44 , No. 12, pps. 2391 - 2404, 2014. Available here as PDF file.

  42. L. Feng, Y. S. Ong and M. H. Lim, "Extreme Learning Machine Guided Memetic Computation for Vehicle Routing", IEEE Intelligent Systems, Vol. 28, No. 6, 2013.

  43. M. Ellabaan, Y. S. Ong, S.D. Handoko, C.K. Kwoh, and H.Y. Man, "Discovering Unique, Low-energy Transition States Using Evolutionary Molecular Memetic Computing", IEEE Computational Intelligence Magazine, Vol. 8, No. 3, pps. 54-63, 2013.

  44. 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, Vol. 7, No. 1, pp. 20-35, 2012. Available here as PDF file. *Bestowed the 2015 IEEE CIS Outstanding Computational Intelligence Magazine Paper Award.

  45. X. S. Chen, Y. S. Ong, "A Conceptual Modeling of Meme Complexes in Stochastic Search", IEEE Transactions On Systems, Man and Cybernetics - Part C, Vol. 42, No. 3, 2012. Available here as PDF file.

  46. S.D. Handoko, X. Ouyang, C.T.T. Su, C.K. Kwoh, and Y. S. Ong, "QuickVina: Accelerating AutoDock Vina Using Gradient-based Heuristics for Global Optimization", IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 9, No. 5, pp. 1266-1272, 2012. Available here as PDF file.

  47. M. Ellabaan, S.D. Handoko, Y. S. Ong, C.K. Kwoh, S. Bahnassy, F. Elassawy, and H.Y. Man, "A Tree-structured Covalent-bond-driven Molecular Memetic Algorithm for Optimization of Ring-deficient Molecules", Computers and Mathematics with Applications, Vol. 64, No. 12, 2012. Available here as PDF file.

  48. G. Iacca, F. Neri, E. Mininno, Y. S. Ong, and M. H. Lim, "Ockham's Razor in memetic computing: Three stage optimal memetic exploration", Information Sciences, 188, pp. 17 - 43, 2012. Available here as PDF file.

  49. X. S. Chen, Y. S. Ong, M. H. Lim and K. C. Tan, "A Multi-Facet Survey on Memetic Computation", IEEE Transactions on Evolutionary Computation, Vol. 15, No. 5, pp. 591 - 607, Oct 2011. Available here as PDF file.

  50. X. S. Chen, L. Feng and Y. S. Ong, "A Self-Adaptive Memeplexes Robust Search Scheme for solving Stochastic Demands Vehicle Routing Problem", International Journal of Systems Science, 43:1347-1366, 2012. Available here as PDF file.

  51. X. S. Chen, Y. S. Ong, M. H. Lim and S. P. Yeo. "Cooperating memes for vehicle routing problems", International Journal of Innovative Computing, Information and Control, Vol. 7, No. 11, pp. 6483 – 6506, 2011. Available here as PDF file.

  52. Y. S. Ong, M. H. Lim and X. S. Chen, "Research Frontier: Memetic Computation - Past, Present & Future", IEEE Computational Intelligence Magazine, Vol. 5, No. 2, pp. 24 -36, 2010. Available here as PDF file.

  53. S.D. Handoko, C.K. Kwoh, and Y. S. Ong, "Feasibility Structure Modeling: An Effective Chaperon for Constrained Memetic Algorithms", IEEE Transactions on Evolutionary Computation, Vol. 14, No. 5, pp. 740-758, Jun 2010. Available here as PDF file.

  54. H. Soh, Y. S. Ong, Q. C. Nguyen, Q. H. Nguyen, M. S. Habibullah, T. Hung and J.-L. Kuo, "Discovering Unique, Low-Energy Pure Water Isomers: Memetic Exploration, Optimization and Landscape Analysis", IEEE Transactions on Evolutionary Computation, Vol. 14, No. 3, pp. 419-437, Jun 2010. Available here as PDF file.

  55. Z. Zhu, Y. S. Ong and J. Zurada, "Identification of Full and Partial Class Relevant Genes", IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 7, No. 2, pp. 263-277, 2010. Near In-Print Version available here as PDF file or Final In-Print Version Available here PDF File.

  56. M. N. Le, Y. S. Ong, Y. Jin & B. Sendhoff, "Lamarckian memetic algorithms: local optimum and connectivity structure analysis", Memetic Computing Journal, Vol. 1, No. 3, pp. 175-190, 2009. Available here as PDF file. *Source code Download*.

  57. R. Meuth, M. H. Lim, Y. S. Ong and D. C. Wunsch II, "A proposition on memes and meta-memes in computing for higher-order learning", Memetic Computing Journal, Vol. 1, No. 2, pp. 85-100, June, 2009. Available here as PDF file or at Springer as PDF File.

  58. Q. H. Nguyen, Y. S. Ong and M. H. Lim, "A Probabilistic Memetic Framework", IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3, pp. 604-623, June 2009. Available here as PDF file or at IEEE Xplore as PDF file. *Source code Download*.*Bestowed the 2012 IEEE CIS Outstanding Transactions on Evolutionary Computation Paper Award

  59. Q. H. Nguyen, Y. S. Ong, M. H. Lim, N. Krasnogor, "Adaptive Cellular Memetic Algorithms", Evolutionary Computation Journal, Vol. 17, No. 2, pp. 231-256, 2009. Available here as PDF file. Available at MIT Press. *Source code Download*.

  60. Q. C. Nguyen, Y. S. Ong and J.-L. Kuo, "A Hierarchical Approach to Study the Thermal Behavior of Protonated Water Clusters H+(H2O)n", Journal Chemical Theory & Computation, Vol. 5, No. 10, pp. 2629-2639, 2009.

  61. Q. C. Nguyen, Y. S. Ong, H. Soh and Jer-Lai Kuo, "Multiscale Approach to Explore the Potential Energy Surface of Water Clusters (H2O)8 n<=8", Journal of Phys. Chem. A, Vol. 112, No. 28, pp. 6257 - 6261, 2008.

  62. Z. Z. Xie, Y. S. Ong and J. L. Kuo, "On the effects of basis-set in studying the Hydration and Dissociation of HF in cubic HF(H2O)7 clusters", Chemical Physics Letters, Vol. 453, No. 1-3, February 2008, pp. 13-17. Available here.

  63. Z. Zhu, Y. S. Ong and M. Dash, "Markov Blanket-Embedded Genetic Algorithm for Gene Selection", Pattern Recognition, Vol. 40, No. 11, pp. 3236-3248, Nov 2007. Available here as PDF file. *Source code Download*

  64. Z. Zhu, Y. S. Ong and M. Dash, "Wrapper-Filter Feature Selection Algorithm Using A Memetic Framework", IEEE Transactions On Systems, Man and Cybernetics - Part B, Vol. 37, No. 1, pp. 70-76, Feb 2007. Available here as PDF file. *Source code Download*

  65. F. Neri, J. Toivanen, G. L. Cascella and Y. S. Ong, "An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies", IEEE/ACM Transactions on Computational Biology and Bioinformatics, Special Issue on Computational Intelligence Approaches in Computational Biology and Bioinformatics, Vol. 4, No. 2, pp. 264-278, April 2007. Available here as PDF file.

  66. J. Tang, M. H. Lim and Y. S. Ong, "Diversity-Adaptive Parallel Memetic Algorithm for Solving Large Scale Combinatorial Optimization Problems", Soft Computing Journal, Vol. 11, No. 9, pp. 873-888, July 2007. Available here as PDF file.

  67. K. K. Lim, Y. S. Ong, M. H. Lim, X. Chen and A. Agarwal, "Hybrid Ant Colony Algorithm for Path Planning in Sparse Graphs", Soft Computing Journal, pp. 981-994, Nov 2007. Available here as PDF file. *Benchmark Problem Download*

  68. X. F. Fan, Z. Zhu, Y. S. Ong, Y. M. Lu, Z. X. Shen, and Jer-Lai Kuo, "A Direct First Principle Study on the Structure and Electronic Properties of BexZn1-xO", Applied Physics Letter 91, 121121, September 2007. Available here.

  69. Z. Z. Zhou, Y. S. Ong, M. H. Lim and B. S. Lee, "Memetic Algorithm using Multi-Surrogates for Computationally Expensive Optimization Problems", Soft Computing Journal, Vol. 11, No. 10, pp. 957-971, August 2007. Available here as PDF file or from Springer. *Source code Download*

  70. J. Tang, M. H. Lim and Y. S. Ong, "Parallel Memetic Algorithm with Selective Local Search for Large Scale Quadratic Assignment Problems", International Journal of Innovative Computing, Information and Control, Vol. 2, No. 6, pp. 1399-1416, Dec 2006.

  71. Y. S. Ong, M. H. Lim, N. Zhu and K. W. Wong, "Classification of Adaptive Memetic Algorithms: A Comparative Study", IEEE Transactions On Systems, Man and Cybernetics - Part B, Vol. 36, No. 1, pp. 141-152, February 2006. Available here as PDF file.

  72. Z. Ning, Y. S. Ong, K. W. Wong and K. T. Seow, "Using Memetic Algorithms For Fuzzy Modelling", Australian Journal on Intelligent Information Processing, Special Issue on Intelligent Technologies, Vol. 8, No. 3, pp. 147-154, Dec 2004. Available here as PDF file.

  73. Y. S. Ong and A.J. Keane, "Meta-Lamarckian Learning in Memetic Algorithm", IEEE Transactions On Evolutionary Computation, Vol. 8, No. 2, pp. 99-110, April 2004. *Featured by Thomson Scientific's Essential Science Indicators as one of the most cited papers in August 2007. Available here as PDF file.



EVOLUTIONARY OPTIMIZATION meets MACHINE LEARNING

  1. L Feng, W. Zhou, W. Liu, Y. S. Ong and K. C. Tan, "Solving Dynamic Multi-objective Problem via Autoencoding Evolutionary Search", IEEE Transactions on Cybernetics, In Press.

  2. L. Feng, Y. Huang, I. W. Tsang, A. Gupta, K. Tang, K. C. Tan and Y. S. Ong, "Towards Faster Vehicle Routing by Transferring Knowledge From Customer Representation", IEEE Transactions on Intelligent Transportation Systems, In Press, 2020

  3. B. Da, A. Gupta, Y. S. Ong, "Curbing Negative Influences Online for Seamless Transfer Evolutionary Optimization", IEEE Transactions on Cybernetics, Vol. 49, No. 12, pps. 4365-4378, 2019. Paper available here as PDF file. Source code available at Github.

  4. W. M. Tan, Y. S. Ong, A. Gupta and C. K. Goh, "Multi-Problem Surrogates: Transfer Evolutionary Multiobjective Optimization of Computationally Expensive Problems", IEEE Transactions on Evolutionary Computation, vol. 23, No. 1, pps. 15-28, 2019. Available here as PDF file.*Source code Download

  5. W. M. Tan, R. Sagarna, A. Gupta, Y. S. Ong and C. K. Goh, "Knowledge Transfer through Machine Learning in Aircraft Design", IEEE Computational Intelligence Magazine, In Press, 2017, Available here as PDF file.

  6. L. Feng, Y. S. Ong, S. Jiang and A. Gupta, "Autoencoding Evolutionary Search with Learning across Heterogeneous Problems", IEEE Transactions on Evolutionary Computation, Vol. 21, No. 5, pps. 760 - 772, 2017. Available here as PDF file.

  7. D. Lim, Y. S. Ong, A. Gupta, C. K. Goh and P. S. Dutta, "Towards a new Praxis in Optinformatics targeting knowledge re-use in evolutionary computation: simultaneous problem learning and optimization", Evolutionary Intelligence, Available here as PDF file, Vol. 9, No. 4, pps. 203-220, 2016.

  8. A. Kattan, A. Agapitos, Y. S. Ong, A. A. Alghamedi and M. O'Neill, "GP Made Faster with Semantic Surrogate Modelling ", Information Sciences, Vol. 355-356, pps. 169-185, 2016.

  9. J. H. Zhong, Y. S. Ong and W. T. Cai, "Self-Learning Gene Expression Programming", IEEE Transactions on Evolutionary Computation, Vol. 20, No. 1, pp. 65-80, 2016.

  10. A. Kattan and Y. S. Ong, "Surrogate Genetic Programming: A Semantic Aware Evolutionary Search", Information Science, Vol. 296, pps. 345-359, 2015.

  11. M. N. Le, Y. S. Ong, S. Menzel, Y. Jin and B. Sendhoff, "Evolution by Adapting Surrogates", Evolutionary Computation Journal, Vol. 1, No. 2, pps. 313-340, 2013. Available here as PDF file.

  12. M. Ellabaan, Y. S. Ong, Q. C. Nguyen and J.-L. Kuo, "Evolutionary Discovery of Transition States in Water Clusters", Journal of Theoretical and Computational Chemistry, Vol. 11, No. 05, Oct 2012. Available here as PDF file.

  13. H. Aydt, S. J. Turner, W. Cai, Y. H. Low, Y. S. Ong and R. Ayani, "Towards an Evolutionary Computing Modeling Language (ECML)", IEEE Transactions on Evolutionary Computation, Vol. 15, No. 2, pp. 230-247, 2011. Available here as PDF file.

  14. D. Lim, Y. Jin, Y. S. Ong and B. Sendhoff, "Generalizing Surrogate-assisted Evolutionary Computation", IEEE Transactions on Evolutionary Computation, Vol. 14, No. 3, pp. 329-355, Jun 2010. Available here as PDF file. *Source code Download*.

  15. Y. S. Ong, K. Y. Lum and P. B. Nair, "Evolutionary Algorithm with Hermite Radial Basis Function Interpolants for Computationally Expensive Adjoint Solvers", Computational Optimization and Applications, Vol. 39, No. 1, January 2008, pp. 97-119 . Available here as PDF file.

  16. Z. Z. Zhou, Y. S. Ong, P. B. Nair, A. J. Keane and K. Y. Lum, "Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization", IEEE Transactions On Systems, Man and Cybernetics - Part C, Vol. 37, No. 1, Jan. 2007, pp. 66-76. Available here as PDF file. *Source code Download*

  17. D. Lim, Y. S. Ong, Y. Jin, B. Sendhoff and B. S. Lee, "Efficient Hierarchical Parallel Genetic Algorithms Using Grid Computing", Future Generation Computer Systems: The International Journal of Grid Computing: Theory, Methods and Applications, Vol. 23, No. 4, pp. 658-670, 2007. Available here as PDF file.

  18. D. Lim, Y. S. Ong, Y. Jin, B. Sendhoff, and B. S. Lee, "Inverse Multi-objective Robust Evolutionary Design", Genetic Programming and Evolvable Machines Journal, Vol. 7, No. 4, pp. 383-404, December, 2006. Available here as PDF file.

  19. Y. S. Ong, P. B. Nair and K. Y. Lum, "Max-Min Surrogate-Assisted Evolutionary Algorithm for Robust Design", IEEE Transactions on Evolutionary Computation, Vol. 10, No. 4, pp. 392-404, August 2006. Available here as PDF file.

  20. Y. S. Ong, P.B. Nair and A.J. Keane, "Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling", American Institute of Aeronautics and Astronautics Journal, 2003, Vol. 41, No. 4, pp. 687-696. Available here as PDF file. *Source code Download*



PATENTS

  1. Method and Apparatus for Automatic Configuration of Meta-heuristics in a Problem Solving Environment, Filed Date: 22 Dec 2006.

  2. A Dual Surrogate Memetic Framework for Single/Multi-Objective Evolutionary Optimization of Computationally Expensive Problems, Filed Date: 11 Oct 2007. Application No./Patent No. : 07118276.0-1225.



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