JOURNAL PAPERS

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

  1. 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.

  2. 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.

  3. R. Chandra, Y. S. Ong, A. Gupta and C. K. Goh, "Evolutionary multi-task learning for modular knowledge representation in neural networks", Neural Processing Letters, In Press, 2017.

  4. 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, In Press, 2017.

  5. 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.

  6. 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, In Press 2017.

  7. 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, In Press 2017.

  8. 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.

  9. R. Chandra, Y. S. Ong and C. K. Goh, "Co-evolutionary multi-task learning with predictive recurrence for multi-step chaotic time series prediction", Neurocomputing, Vol. 243, pps. 21-34, 2017.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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


  16. 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.

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

  18. 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.

  19. 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.

  20. 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.

  21. 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.

  22. 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.

  23. 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.

  24. 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.

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

  26. 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.

  27. 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.

  28. 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.

  29. 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.

  30. 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.

  31. 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.

  32. 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.

  33. 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.

  34. 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.

  35. 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.

  36. 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.

  37. 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.

  38. 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.

  39. 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.

  40. 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.

  41. 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*.

  42. 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.

  43. 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. *Bestowed the 2012 IEEE CIS Outstanding Transactions on Evolutionary Computation Paper Award Available here as PDF file or at IEEE Xplore as PDF file. *Source code Download*.

  44. 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*.

  45. 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.

  46. 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.

  47. 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.

  48. 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*

  49. 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*

  50. 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.

  51. 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.

  52. 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*

  53. 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.

  54. 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*

  55. 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.

  56. 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.

  57. 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.

  58. 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. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

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

  7. 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.

  8. 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.

  9. 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.

  10. 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*.

  11. 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.

  12. 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*

  13. 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.

  14. 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.

  15. 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.

  16. 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*



ARTIFICIAL INTELLIGENCE - MACHINE LEARNING & APPLICATIONS

  1. R. Chandra, Y. S. Ong, A. Gupta and C. K. Goh, "Evolutionary multi-task learning for modular knowledge representation in neural networks", Neural Processing Letters, In Press, 2017.

  2. 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, In Press, 2017.

  3. 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, In Press, 2017.

  4. 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, In Press, 2017.

  5. 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.

  6. 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.

  7. 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, pp. 2367 - 2380, 2016.

  8. I. Chaturvedi, Y. S. Ong IW 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  21. 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.

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.