References

  • Chandra, R., Gupta, A., Ong, Y. S., and Goh, C. K., "Evolutionary Multi-task Learning for Modular Knowledge Representation in Neural Networks", Neural Processing Letters, 2017, 1-17.

  • Elarbi, M., Bechikh, S., Gupta, A., Said, L. B., and Ong, Y. S., "A New Decomposition-Based NSGA-II for Many-Objective Optimization", IEEE Transactions on Systems, Man, and Cybernetics: Systems, In Press, 2017.

  • Chandra, R., Ong, Y. S., and Goh, C. K., "Co-Evolutionary Multi-Task Learning for Dynamic Time Series Prediction", arXiv preprint arXiv:1703.01887, 2017.

  • Min, A. T. W., Sagarna, R., Gupta, A., Ong, Y. S., and Goh, C. K., "Knowledge Transfer Through Machine Learning in Aircraft Design", IEEE Computational Intelligence Magazine, 12(4), 48-60.

  • Scott, E. O., and De Jong, K. A., "Multitask Evolution with Cartesian Genetic Programming", arXiv preprint arXiv:1702.02217, 2017.

  • Yuan, Y., Ong, Y. S., Gupta, A., and Xu, H., "Objective Reduction in Many-Objective Optimization: Evolutionary Multiobjective Approaches and Comprehensive Analysis", IEEE Transactions on Evolutionary Computation, In Press, 2017.

  • Tang, Z., Gong, M., and Zhang, M., "Evolutionary Multi-task Learning for Modular Extremal Learning Machine", IEEE CEC 2017, pp. 474-479.

  • Liaw, R. T., and Ting, C. K., "Evolutionary Many-tasking Based on Biocoenosis through Symbiosis: A Framework and Benchmark Problems", IEEE CEC 2017, pp. 2266-2273.

  • Bali, K. K., Gupta, A., Feng, L., Ong, Y. S., and Siew, T. P., "Linearized Domain Adaptation in Evolutionary Multitasking", IEEE CEC 2017, pp. 1295-1302.

  • Feng, L., Zhou, W., Zhou, L., Jiang, S.W., Zhong, J.H., Da, B.S., Zhu, Z.X. and Wang, Y., "An Empirical Study of Multifactorial PSO and Multifactorial DE", IEEE CEC 2017, pp. 921-928.

  • Wen, Y. W., and Ting, C. K., "Parting Ways and Reallocating Resources in Evolutionary Multitasking", IEEE CEC 2017, pp. 2404-2411.

  • Chandra, R., Ong, Y. S., and Goh, C. K., "Co-evolutionary Multi-task Learning with Predictive Recurrence for Multi-Step Chaotic Time Series Prediction". Neurocomputing 243, 2017, 21-34.

  • L. Feng, Y. S. Ong, S. Jiang and A. Gupta, "Autoencoding Evolutionary Search with Learning across Heterogeneous Problems", IEEE Transactions on Evolutionary Computation, In Press, 2017. Available here to download the paper.

  • 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. Available here to access the paper.

  • K. Bali, A. Gupta, L. Feng, Y. S. Ong, and P. S. Tan, "Linearized Domain Adaptation in Evolutionary Multitasking", IEEE Congress on Evolutionary Computation, Spain, June 5-8, 2017. Available here to download the paper.

  • A. Gupta, B. Da, Y. Yuan and Y. S. Ong, "On the Emerging Notion of Evolutionary Multitasking: A Computational Analog of Cognitive Multitasking", Recent Advances in Evolutionary Multi-objective Optimization, pps. 139-157, 2017. Available here to access the paper.

  • L. Zhou, L. Feng, J. Zhong, Y. S. Ong, Z. Zhu, E. Sha, "Evolutionary Multitasking in Combinatorial Search Spaces: A Case Study in Capacitated Vehicle Routing Problem", IEEE SSCI 2016, Athens, Greece. Available here to download the paper.

  • Sagarna, R., and Ong, Y. S., "Concurrently Searching Branches in Software Tests Generation through Multitask Evolution", SSCI 2016, pp. 1-8.

  • Zheng, X., Lei, Y., Gong, M., and Tang, Z., "Multifactorial Brain Storm Optimization Algorithm", Bio-Inspired Computing-Theories and Applications 2016, pp. 47-53, Springer, Singapore.

  • Y. Yuan, Y. S. Ong, A. Gupta, P. S. Tan, H. Xu, "Evolutionary Multitasking in Permutation-Based Combinatorial Optimization Problems: Realization with TSP, QAP, LOP, and JSP", accepted in IEEE TENCON 2016. Available here to download the paper.

  • A. Gupta, and Y. S. Ong, "Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization". IEEE SSCI 2016, Athens, Greece. Available here to download the paper.

  • Ong, Y. S., "Towards Evolutionary Multitasking: A New Paradigm in Evolutionary Computation", Computational Intelligence, Cyber Security and Computational Models, pp. 25-26, Springer, Singapore, 2016.

  • R. Chandra, A. Gupta, Y. S. Ong, and C. K. Goh, "Evolutionary multi-task learning for modular training of feedforward neural networks", ICONIP 2016. Available here as PDF file.

  • A. Gupta, Y. S. Ong, B. Da, L. Feng, and S. D. Handoko, "Measuring Complementarity between Function Landscapes in Evolutionary Multitasking", IEEE WCCI - Congress on Evolutionary Computation, 2016. Available here as PDF file.

  • Wen, Y. W., and Ting, C. K., "Learning Ensemble of Decision Trees through Multifactorial Genetic Programming", IEEE WCCI - Congress on Evolutionary Computation, 2016.

  • B. Da, A. Gupta, Y. S. Ong, L. Feng, and C. Wang, "Evolutionary Multitasking Across single and Multi-Objective Formulations for Improved Problem Solving", IEEE WCCI - Congress on Evolutionary Computation, 2016. Available here as PDF file.

  • A. Gupta, Y. S. Ong, L. Feng and K. C. Tan, "Multi-Objective Multifactorial Optimization in Evolutionary Multitasking", IEEE Transactions on Cybernetics, 2016. Available here as PDF file.

  • Y. S. Ong and A. Gupta, "Evolutionary Multitasking: A Computer Science View of Cognitive Multitasking", Cognitive Computation, 10.1007/s12559-016-9395-7, pp 1-18, 2016. Click here to download the paper.

  • Da, B., Gupta, A., Ong, Y.S. and Feng, L., "The Boon of Gene-Culture Interaction for Effective Evolutionary Multitasking". In Australasian Conference on Artificial Life and Computational Intelligence, pp. 54-65. Springer, Cham, 2016.

  • Jiang, S., Xu, C., Gupta, A., Feng, L., Ong, Y. S., Zhang, A. N., and Tan, P. S., "Complex and Intelligent Systems in Manufacturing", IEEE Potentials 2016, 35(4), 23-28.

  • A. Gupta, J. Mańdziuk, Y. S. Ong, 'Evolutionary multitasking in bi-level optimization', Complex and Intelligent Systems, Volume 1, Issue 1-4 , pp 83-95, 10.1007/s40747-016-0011-y, 2015. Click here to download the paper.

  • A. Gupta, Y. S. Ong, L. Feng, 'Multifactorial Evolution: Towards Evolutionary Multitasking', IEEE Transactions on Evolutionary Computation, Accepted, 2015. Click here to download the paper.

  • D. Lim, Y. S. Ong, A. Gupta, C. K. Goh, P. S. Dutta, "Towards a new Praxis in optinformatics targeting knowledge re-use in evolutionary computation: simultaneous problem learning and optimization", Evolutionary Intelligence, 10.1007/s12065-016-0146-1, pp 1-18, 2016. Available here to access the paper.

  • L. Feng, Y. S. Ong, A. H. Tan, and I. W. Tsang, 'Memes as Building Block for Evolutionary Optimization of Problem Instances', Memetic Computing Journal, vol. 7, no. 3, pp. 159-180, 2015. Click here to download the paper.

  • 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, 2014. Click here to download the paper.

  • M. N. Le, Y. S. Ong, C. W. Seah, S. Menzel and B. Sendhoff, 'Multi Co-objective Evolutionary Optimization: Cross Surrogate Augmentation for Computationally Expensive Problems', IEEE Congress on Evolutionary Computation, June 2012. Click here to download the paper.