Dr Rohitash Chandra

F09 - Madsen Building
The University of Sydney

Telephone 0286276033

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Biographical details

Dr. Rohitash Chandra is USyd Research Fellow at the School of Geosciences and Centre for Translational Data Science. He holds a Ph.D in Artificial Intelligence (2012) from Victoria University of Wellington, M.S. in Computer Science (2008) from the University of Fiji, and B.Sc. in Computer Science and Engineering Technology (2006) from the University of the South Pacific, Fiji. Dr. Chandra has taken roles as Postdoctoral Research Fellow in Bioinformatics at Victoria University of Wellington (Janaury to June 2012), Lecturer in Computing Science at the University of the South Pacific (2013- 2015), Research Fellow in Machine Learning at Rolls Royce @NTU Corporate Lab, Nanyang Technological University, SIngapore (2016). Dr. Chandra is from Nausori, Fiji with a Girmit heritage.

Research interests

Dr. Chandra's research interests are in areas of deep learning, neuro-evolution, Bayesian methods, solid Earth Evolution, reef modelling and mineral exploration. Currently, he is involved in projects that employ machine learning and Bayesian inference via parallel tempering for solid Earth evolution, mineral exploration, and reef modelling.

Teaching and supervision

Current projects

Technology Development

  1. Neural networks and learning algorithms: Neural networks are loosely modelled after biological neural systems and have a wide range of data driven applications that include time series prediction and pattern recognition. Opposed to gradient based methods, neuro-evolution features evolutionary algorithms that provide a black-box approach to learning in neural networks. Hence, the learning algorithm is not constrained to the architecture of the network and does not face the limitations of gradient descent such as local minima and vanishing gradients. I have been developing novel neural network learning algorithms using neuro-evolution with motivations from transfer learning, multi-task learning and reinforcement learning. I have been using feedforward and recurrent neural networks with application to a wide range of time series problems that include multidimensional and multi-step ahead prediction with applications that include predicting the behaviour of extreme events such as cyclones. The challenge is in problems that have missing information, noise and inconsistencies in the organisation of data. Collaboration: Prof. Yew Soon Ong, School of Computer Science and Engineering, Nanyang Technological University; Prof. Junbin Gao, Business School, University of Sydney; Prof. Christian Omlin, University of Agder, Norway.

  1. Evolutionary optimisation: Evolutionary algorithms used for optimisation are inspired from the theory of evolution. The major feature of these algorithms is their applicability in large scale problems, particularly that do not have the feature to use gradient information to form new proposals. I have contributed mostly to the field of cooperative coevolution and problem decomposition for neuro-evolution and large scale global optimisation problems. I would like to extend this field further with Bayesian methods that have a natural way for uncertainty quantification which could address the limitation of convergence in evolutionary optimization and related stochastic and metaheuristic algorithms. Collaboration: Prof. Mengie Zhang, Victoria University of Wellington, New Zealand; Prof. Yew Soon Ong, School of Computer Science and Engineering, Nanyang Technological University, Singapore.

  2. Bayesian neural networks: Markov Chain Monte Carlo (MCMC) methods provide a probabilistic approach for estimation of the free parameters in a wide range of models. Parallel tempering is a MCMC method that features parallelism with enhanced exploration capabilities. It features a number of replicas with slight variations in the acceptance criteria. More recently, I have been developing algorithms for Bayesian neural networks that feature parallel tempering and parallel computing in order to address computationally expensive problems. The challenge is in the inference for deep learning network architectures that features millions of parameters. Collaboration: Prof. Sally Cripps, School of Mathematics and Statistics, University of Sydney.

  3. Surrogate-assisted inference and optimisation: Surrogate-assisted optimization considers the estimation of an objective function for models given computational inefficiency or difficulty to obtain clear results. Surrogate-assistance inference addresses inefficiency of parallel tempering for large-scale problems by combining parallel computing features with surrogate assisted estimation of likelihood function that describes the plausibility of a model parameter value, given specific observed data. I have been developing these methods for large-scale Bayesian neural networks and also for computationally expensive Geoscientific models such as landscape evolution models. The challenge is to have a good estimation by the surrogates when the actual model features hundreds of free parameters. Collaboration: Prof. Dietmar Muller, School of Geosciences, University of Sydney; Prof. Yew Soon Ong, School of Computer Science and Engineering, Nanyang Technological University, Singapore.

Applications
  1. Solid Earth evolution: Bayesian inference has been a popular methodology for the estimation and uncertainty quantification of parameters in geological and geophysical forward models. Badlands is a basin and landscape evolution forward model for simulating topography evolution at a large range of spatial and time scales. Our solid Earth evolution projects consider Bayesian inference for parameter estimation and uncertainty quantification for landscape dynamics model (Bayeslands). The challenge is in parameter estimation for computationally expensive models which are being addressed by high-performance computing and surrogate-assisted Bayesian inversion. Collaboration: Prof. Dietmar Muller and Dr. Tristan Salles, School of Geosciences, University of Sydney.

  2. Reef modelling: Geological reef models such as Py-Reef-Core provides insights into the flux of carbon by analysing carbonate platform growth and demise through time, and modelling their evolution using landscape dynamics and reef modelling. We provide uncertainty quantification estimation of free parameters using Bayesian inference for reef modelling (BayesReef). Bayesian inference via MCMC and parallel tempering is used with Py-Reef-Core model to understand reef evolution on a geological timescale that can help in predicting the future evolution of coral reefs. The challenge here is in the estimation of the parameters which involves highly non-separable and constrained optimisation. Collaboration: A/Prof. Jody Webster and Dr. Tristan Salles, School of Geosciences, University of Sydney.

  3. Mineral exploration: The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes are of interest in terms of mineralization. Although a wide range of applications utilized computer vision techniques, a standard workflow for application of these techniques to mineral exploration is lacking. We use computer vision techniques for extracting geological lineaments using optical remote sensing data. Furthermore, in another research direction, we provide a synergy of geophysical forward models and Bayesian inference for 3D joint inversion for mineral prospecting and exploration. Collaboration: Prof. Dietmar Muller, Ehsan Farahbakhsh, and Prof. Gregory Houseman, School of Geosciences, University of Sydney. Dr. Hugo Olierook, Prof. Chris Clark, and Prof. Steven Reddy, Curtin University. Dr. Richard Scalzo and Prof. Sally Cripps, Centre for Translational Data Science, University of Sydney.

  4. Paleoclimate reconstruction: The reconstruction of paleoclimate precipitation can provide light to Earth’s climate history of millions of years in the past. Although global circulation models have been used with success for reconstruction of precipitation in the Miocene period, their application to an era back in time is a major challenge due to limited data. We use an alternate approach that features machine learning methods to predict precipitation that define paleoclimate that spans up to 400 millions of year in the past. The data features a range of geological indicators including sedimentary deposits (coal, evaporates, glacial deposits). The challenge has been in addressing missing values in the dataset and providing rigorous uncertainty quantification in order to develop paleo-maps of forests and vegetation. Collaboration: Prof. Dietmar Muller, School of Geosciences, and Prof. Sally Cripps, Centre for Translational Data Science, University of Sydney.

Associations

  1. Member, IEEE
  2. Member, EarthBye Group, School of Geoscience, The University of Sydney
  3. Member, Geocoastal Research Group, School of Geoscience, The University of Sydney
  4. Member, ARC Basin Genesis Hub, School of Geoscience, The University of Sydney

Awards and honours

  1. Doctoral Completion Award, VictoriaUniversity of Wellington (2012)
  2. University of Sydney Fellowship Award (2017 - 2019)

In the media

Selected grants

2017

  • Multi-Source transfer learning for time series prediction in areas of climate change; Chandra R; DVC Research/Postdoctoral Research Fellowship Scheme.

Selected publications

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Book Chapters

  • Rolland, L., Chandra, R. (2010). On solving the forward kinematics of the 6-6 General parallel manipulator with an efficient evolutionary algorithm. In Werner Schiehlen, Vincenzo Parenti-Castelli (Eds.), ROMANSY 18 - Robot Design, Dynamics and Control: Proceedings of the Eighteenth CISM-IFToMM Symposium (CISM International Centre for Mechanical Sciences), (pp. 117-124). Springer.

Journals

  • Jain, K., Chandra, R., Deo, R., Cripps, S. (2019). Langevin-gradient parallel tempering for Bayesian neural learning [Forthcoming]. Neurocomputing. [More Information]
  • Farahbakhsh, E., Chandra, R., Eslamkish, T., Muller, R. (2019). Modeling geochemical anomalies of stream sediment data through a weighted drainage catchment basin method for detecting porphyry Cu-Au mineralization. Journal Of Geochemical Exploration, 204(September 2019), 12-32. [More Information]
  • Chandra, R., Ong, Y., Goh, C. (2018). Co-evolutionary multi-task learning for dynamic time series prediction. Applied Soft Computing, 70, 576-589. [More Information]
  • Chandra, R., Cripps, S. (2018). Coevolutionary multi-task learning for feature-based modular pattern classification. Neurocomputing, 319, 164-175. [More Information]
  • Chandra, R., Gupta, A., Ong, Y., Goh, C. (2018). Evolutionary Multi-task Learning for Modular Knowledge Representation in Neural Networks. Neural Processing Letters, 47(3), 993-1009. [More Information]
  • Chandra, R., Ong, Y., Goh, C. (2017). Co-evolutionary multi-task learning with predictive recurrence for multi-step chaotic time series prediction. Neurocomputing, 243, 21-34. [More Information]
  • Chaudhry, S., Chandra, R. (2017). Face detection and recognition in an unconstrained environment for mobile visual assistive system. Applied Soft Computing, 53, 168-180. [More Information]
  • Chandra, R., Chand, S. (2016). Evaluation of co-evolutionary neural network architectures for time series prediction with mobile application in finance. Applied Soft Computing, 49, 462-473. [More Information]
  • Rolland, L., Chandra, R. (2016). The forward kinematics of the 6-6 parallel manipulator using an evolutionary algorithm based on generalized generation gap with parent-centric crossover. Robotica, 34(1), 1-22. [More Information]
  • Chandra, R. (2015). Competition and Collaboration in Cooperative Coevolution of Elman Recurrent Neural Networks for Time-Series Prediction. IEEE Transactions on Neural Networks and Learning Systems, 26(12), 3123-3136.
  • Chandra, R., Rolland, L. (2015). Global-Local Population Memetic Algorithm for Solving the Forward Kinematics of Parallel Manipulators. Connection Science, 27(1), 22-39. [More Information]
  • Chandra, R. (2014). Memetic cooperative coevolution of Elman recurrent neural networks. Soft Computing, 18(8), 1549-1559. [More Information]
  • Chandra, R., Frean, M., Zhang, M. (2012). Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks. Soft Computing, 16(6), 1009-1020. [More Information]
  • Chandra, R., Zhang, M. (2012). Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing, 86, 116-123. [More Information]
  • Chandra, R., Frean, M., Zhang, M. (2012). Crossover-based local search in cooperative co-evolutionary feedforward neural networks. Applied Soft Computing, 12(9), 2924-2932. [More Information]
  • Chandra, R., Frean, M., Zhang, M. (2012). On the issue of separability for problem decomposition in cooperative neuro-evolution. Neurocomputing, 87, 33-40. [More Information]
  • Chandra, R., Frean, M., Zhang, M., Omlin, C. (2011). Encoding subcomponents in cooperative co-evolutionary recurrent neural networks. Neurocomputing, 74(17), 3223-3234. [More Information]
  • Chandra, R., Rolland, L. (2011). On solving the forward kinematics of 3RPR planar parallel manipulator using hybrid metaheuristics. Applied Mathematics and Computation, 217(22), 8997-9008. [More Information]
  • Rohitash, C., Knight, R., Omlin, C. (2009). Renosterveld Conservation in South Africa: A Case Study for Handling Uncertainty in Knowledge Based Neural Networks for Environmental Management. Journal of Environmental Informatics, 13(1), 56-65. [More Information]

Conferences

  • Chandra, R., Cripps, S. (2018). Bayesian Multi-task Learning for Dynamic Time Series Prediction. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Zhang, Y., Chandra, R., Gao, J. (2018). Cyclone Track Prediction with Matrix Neural Networks. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Wong, G., Sharma, A., Chandra, R. (2018). Information Collection Strategies in Memetic Cooperative Neuroevolution for Time Series Prediction. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R. (2018). Multi-Task Modular Backpropagation for Dynamic Time Series Prediction. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Azizi, L., Cripps, S. (2017). Bayesian neural learning via langevin dynamics for chaotic time series prediction. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]
  • Chandra, R. (2017). Co-evolutionary multi-task learning for modular pattern classification. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part VI), Cham: Springer. [More Information]
  • Chandra, R. (2017). Dynamic cyclone wind-intensity prediction using co-evolutionary multi-task learning. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]
  • Chandra, R. (2017). Multi-task modular backpropagation for feature-based pattern classification. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part VI), Cham: Springer. [More Information]
  • Chandra, R. (2017). Towards an affective computational model for machine consciousness. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]
  • Chandra, R., Deo, R., Omlin, C. (2016). An architecture for encoding two-dimensional cyclone track prediction problem in coevolutionary recurrent neural networks. 2016 International Joint Conference on Neural Networks (IJCNN 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Dayal, K., Rollings, N. (2016). Application of cooperative neuro-evolution of Elman recurrent networks for a two-dimensional cyclone track prediction for the south pacific region. 2015 International Joint Conference on Neural Networks (IJCNN 2015), Killarney: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Hussein, S., Chandra, R. (2016). Chaotic feature selection and reconstruction in time series prediction. The 23rd International Conference on Neural Information Processing (ICONIP 2016), Cham, Switzerland: Springer International Publishing. [More Information]
  • Nand, R., Chandra, R. (2016). Coevolutionary feature selection and reconstruction in neuro-evolution for time series prediction. 2nd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2016), Cham, Switzerland: Springer International Publishing Switzerland. [More Information]
  • Nand, R., Chandra, R. (2016). Competitive Island cooperative neuro-evolution of feedforward networks for time series prediction. 2nd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2016), Cham, Switzerland: Springer International Publishing Switzerland. [More Information]
  • Chandra, R., Wong, G. (2016). Competitive two-island cooperative co-evolution for training feedforward neural networks for pattern classification problems. 2015 International Joint Conference on Neural Networks (IJCNN 2015), Killarney: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Bali, K., Chandra, R., Omidvar, M. (2016). Contribution based multi-island competitive cooperative coevolution. 2016 IEEE Congress on Evolutionary Computation (CEC 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Rana, M., Chandra, R., Agelidis, V. (2016). Cooperative neuro-evolutionary recurrent neural networks for solar power prediction. 2016 IEEE Congress on Evolutionary Computation (CEC 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Gupta, A., Ong, Y., Goh, C. (2016). Evolutionary multi-task learning for modular training of feedforward neural networks. 23rd International Conference on Neural Information Processing (ICONIP 2016), Cham: Springer. [More Information]
  • Deo, R., Chandra, R. (2016). Identification of minimal timespan problem for recurrent neural networks with application to cyclone wind-intensity prediction. 2016 International Joint Conference on Neural Networks (IJCNN 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Wong, G., Chandra, R., Sharma, A. (2016). Memetic cooperative neuro-evolution for chaotic time series prediction. The 23rd International Conference on Neural Information Processing (ICONIP 2016), Cham, Switzerland: Springer International Publishing. [More Information]
  • Hussein, S., Chandra, R., Sharma, A. (2016). Multi-step-ahead chaotic time series prediction using coevolutionary recurrent neural networks. 2016 IEEE Congress on Evolutionary Computation (CEC 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Deo, R., Bali, K., Sharma, A. (2016). On the relationship of degree of separability with depth of evolution in decomposition for cooperative coevolution. 2016 IEEE Congress on Evolutionary Computation (CEC 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Nand, R., Chandra, R. (2016). Reverse neuron level decomposition for cooperative neuro-evolution of feedforward networks for time series prediction. 2nd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2016), Cham, Switzerland: Springer International Publishing Switzerland. [More Information]
  • Chaudhry, S., Chandra, R. (2016). Unconstrained face detection from a mobile source using convolutional neural networks. 23rd International Conference on Neural Information Processing (ICONIP 2016), Cham: Springer. [More Information]
  • Chandra, R., Dayal, K. (2015). Coevolutionary recurrent neural networks for prediction of rapid intensification in wind intensity of tropical cyclones in the south pacific region. 22nd International Conference on Neural Information Processing (ICONIP 2015), Cham: Springer. [More Information]
  • Bali, K., Chandra, R., Omidvar, M. (2015). Competitive island-based cooperative coevolution for efficient optimization of large-scale fully-separable continuous functions. 22nd International Conference on Neural Information Processing (ICONIP 2015), Cham: Springer. [More Information]
  • Chandra, R., Bali, K. (2015). Competitive two-island cooperative coevolution for real parameter global optimisation. 2015 IEEE Congress on Evolutionary Computation (CEC 2015), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Dayal, K. (2015). Cooperative neuro-evolution of Elman recurrent networks for tropical cyclone wind-intensity prediction in the South Pacific region. 2015 IEEE Congress on Evolutionary Computation (CEC 2015), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Wong, G., Chandra, R. (2015). Enhancing competitive island cooperative neuro-evolution through backpropagation for pattern classification. 22nd International Conference on Neural Information Processing (ICONIP 2015), Cham: Springer. [More Information]
  • Bali, K., Chandra, R. (2015). Multi-island competitive cooperative coevolution for real parameter global optimization. 22nd International Conference on Neural Information Processing (ICONIP 2015), Cham: Springer. [More Information]
  • Chandra, R. (2015). Multi-objective cooperative neuro-evolution of recurrent neural networks for time series prediction. 2015 IEEE Congress on Evolutionary Computation (CEC 2015), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Nand, R., Chandra, R. (2015). Neuron-synapse level problem decomposition method for cooperative neuro-evolution of feedforward networks for time series prediction. 22nd International Conference on Neural Information Processing (ICONIP 2015), Cham: Springer. [More Information]
  • Bali, K., Chandra, R. (2015). Scaling up multi-island competitive cooperative coevolution for real parameter global optimisation. 28th Australasian Joint Conference on Artificial Intelligence (AI 2015), Cham: Springer. [More Information]
  • Chandra, R., Dayal, K. (2015). Two-Dimensional Time Series Approach for Cyclone Track Prediction using Cooperative Co evolution of Recurrent Neural Networks. 2015 International Joint Conference on Neural Networks (IJCNN 2015), Killarney: Institute of Electrical and Electronics Engineers (IEEE).
  • Chandra, R. (2014). Competitive two-island cooperative coevolution for training Elman recurrent networks for time series prediction. 2014 International Joint Conference on Neural Networks (IJCNN 2014), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chand, S., Chandra, R. (2014). Cooperative coevolution of feed forward neural networks for financial time series problem. 2014 International Joint Conference on Neural Networks (IJCNN 2014), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chand, S., Chandra, R. (2014). Multi-objective cooperative coevolution of neural networks for time series prediction. 2014 International Joint Conference on Neural Networks (IJCNN 2014), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Singh, V., Bali, A., Adhikthikar, A., Chandra, R. (2014). Web and mobile based tourist travel guide system for Fiji's tourism industry. 2014 Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE 2014), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R. (2013). Adaptation in Cooperative Coevolutionary Recurrent Neural Networks for Time Series Prediction. 2013 International Joint Conference on Neural Networks (IJCNN 2013), Piscataway: Institute of Electrical and Electronics Engineers (IEEE).
  • Chandra, R. (2013). Adaptive problem decomposition in cooperative coevolution of recurrent networks for time series prediction. 2013 International Joint Conference on Neural Networks (IJCNN 2013), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Zhang, M., Peng, L. (2012). Application of Cooperative Convolution Optimization for 13C Metabolic Flux Analysis: Simulation of Isotopic Labeling Patterns Based on Tandem Mass Spectrometry Measurements. 9th International Conference on Simulated Evolution and Learning (SEAL 2012), Berlin: Springer. [More Information]
  • Chandra, R., Frean, M., Zhang, M. (2011). A memetic framework for cooperative coevolution of recurrent neural networks. 2011 International Joint Conference on Neural Networks (IJCNN 2011), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Frean, M., Zhang, M. (2011). Modularity adaptation in cooperative coevolution of feedforward neural networks. 2011 International Joint Conference on Neural Networks (IJCNN 2011), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Frean, M., Zhang, M. (2010). An Encoding Scheme for Cooperative Co-evolutionary Neural Networks. Proceedings of the 23rd Australasian Joint Conference on Artificial Intelligence.
  • Chandra, R., Frean, M., Rolland, L. (2009). A Hybrid Meta-Heuristic Paradigm for Solving the Forward Kinematics of 6-6 General Parallel Manipulator. 8th IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA 2009).
  • Rolland, L., Chandra, R. (2009). Forward Kinematics of the 3RPR planar Parallel Manipulators Using Real Coded Genetic Algorithms. Proceedings of 24th International Symposium on Computer and Information Sciences.
  • Rolland, L., Chandra, R. (2009). Forward Kinematics of the 6-6 general Parallel Manipulator Using Real Coded Genetic Algorithms. Proceedings of 2009 IEEE/ASME Conference on Advanced Intelligent Mechatronics,.

2019

  • Jain, K., Chandra, R., Deo, R., Cripps, S. (2019). Langevin-gradient parallel tempering for Bayesian neural learning [Forthcoming]. Neurocomputing. [More Information]
  • Farahbakhsh, E., Chandra, R., Eslamkish, T., Muller, R. (2019). Modeling geochemical anomalies of stream sediment data through a weighted drainage catchment basin method for detecting porphyry Cu-Au mineralization. Journal Of Geochemical Exploration, 204(September 2019), 12-32. [More Information]

2018

  • Chandra, R., Cripps, S. (2018). Bayesian Multi-task Learning for Dynamic Time Series Prediction. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Ong, Y., Goh, C. (2018). Co-evolutionary multi-task learning for dynamic time series prediction. Applied Soft Computing, 70, 576-589. [More Information]
  • Chandra, R., Cripps, S. (2018). Coevolutionary multi-task learning for feature-based modular pattern classification. Neurocomputing, 319, 164-175. [More Information]
  • Zhang, Y., Chandra, R., Gao, J. (2018). Cyclone Track Prediction with Matrix Neural Networks. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Gupta, A., Ong, Y., Goh, C. (2018). Evolutionary Multi-task Learning for Modular Knowledge Representation in Neural Networks. Neural Processing Letters, 47(3), 993-1009. [More Information]
  • Wong, G., Sharma, A., Chandra, R. (2018). Information Collection Strategies in Memetic Cooperative Neuroevolution for Time Series Prediction. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R. (2018). Multi-Task Modular Backpropagation for Dynamic Time Series Prediction. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

2017

  • Chandra, R., Azizi, L., Cripps, S. (2017). Bayesian neural learning via langevin dynamics for chaotic time series prediction. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]
  • Chandra, R. (2017). Co-evolutionary multi-task learning for modular pattern classification. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part VI), Cham: Springer. [More Information]
  • Chandra, R., Ong, Y., Goh, C. (2017). Co-evolutionary multi-task learning with predictive recurrence for multi-step chaotic time series prediction. Neurocomputing, 243, 21-34. [More Information]
  • Chandra, R. (2017). Dynamic cyclone wind-intensity prediction using co-evolutionary multi-task learning. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]
  • Chaudhry, S., Chandra, R. (2017). Face detection and recognition in an unconstrained environment for mobile visual assistive system. Applied Soft Computing, 53, 168-180. [More Information]
  • Chandra, R. (2017). Multi-task modular backpropagation for feature-based pattern classification. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part VI), Cham: Springer. [More Information]
  • Chandra, R. (2017). Towards an affective computational model for machine consciousness. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]

2016

  • Chandra, R., Deo, R., Omlin, C. (2016). An architecture for encoding two-dimensional cyclone track prediction problem in coevolutionary recurrent neural networks. 2016 International Joint Conference on Neural Networks (IJCNN 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Dayal, K., Rollings, N. (2016). Application of cooperative neuro-evolution of Elman recurrent networks for a two-dimensional cyclone track prediction for the south pacific region. 2015 International Joint Conference on Neural Networks (IJCNN 2015), Killarney: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Hussein, S., Chandra, R. (2016). Chaotic feature selection and reconstruction in time series prediction. The 23rd International Conference on Neural Information Processing (ICONIP 2016), Cham, Switzerland: Springer International Publishing. [More Information]
  • Nand, R., Chandra, R. (2016). Coevolutionary feature selection and reconstruction in neuro-evolution for time series prediction. 2nd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2016), Cham, Switzerland: Springer International Publishing Switzerland. [More Information]
  • Nand, R., Chandra, R. (2016). Competitive Island cooperative neuro-evolution of feedforward networks for time series prediction. 2nd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2016), Cham, Switzerland: Springer International Publishing Switzerland. [More Information]
  • Chandra, R., Wong, G. (2016). Competitive two-island cooperative co-evolution for training feedforward neural networks for pattern classification problems. 2015 International Joint Conference on Neural Networks (IJCNN 2015), Killarney: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Bali, K., Chandra, R., Omidvar, M. (2016). Contribution based multi-island competitive cooperative coevolution. 2016 IEEE Congress on Evolutionary Computation (CEC 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Rana, M., Chandra, R., Agelidis, V. (2016). Cooperative neuro-evolutionary recurrent neural networks for solar power prediction. 2016 IEEE Congress on Evolutionary Computation (CEC 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Chand, S. (2016). Evaluation of co-evolutionary neural network architectures for time series prediction with mobile application in finance. Applied Soft Computing, 49, 462-473. [More Information]
  • Chandra, R., Gupta, A., Ong, Y., Goh, C. (2016). Evolutionary multi-task learning for modular training of feedforward neural networks. 23rd International Conference on Neural Information Processing (ICONIP 2016), Cham: Springer. [More Information]
  • Deo, R., Chandra, R. (2016). Identification of minimal timespan problem for recurrent neural networks with application to cyclone wind-intensity prediction. 2016 International Joint Conference on Neural Networks (IJCNN 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Wong, G., Chandra, R., Sharma, A. (2016). Memetic cooperative neuro-evolution for chaotic time series prediction. The 23rd International Conference on Neural Information Processing (ICONIP 2016), Cham, Switzerland: Springer International Publishing. [More Information]
  • Hussein, S., Chandra, R., Sharma, A. (2016). Multi-step-ahead chaotic time series prediction using coevolutionary recurrent neural networks. 2016 IEEE Congress on Evolutionary Computation (CEC 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Deo, R., Bali, K., Sharma, A. (2016). On the relationship of degree of separability with depth of evolution in decomposition for cooperative coevolution. 2016 IEEE Congress on Evolutionary Computation (CEC 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Nand, R., Chandra, R. (2016). Reverse neuron level decomposition for cooperative neuro-evolution of feedforward networks for time series prediction. 2nd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2016), Cham, Switzerland: Springer International Publishing Switzerland. [More Information]
  • Rolland, L., Chandra, R. (2016). The forward kinematics of the 6-6 parallel manipulator using an evolutionary algorithm based on generalized generation gap with parent-centric crossover. Robotica, 34(1), 1-22. [More Information]
  • Chaudhry, S., Chandra, R. (2016). Unconstrained face detection from a mobile source using convolutional neural networks. 23rd International Conference on Neural Information Processing (ICONIP 2016), Cham: Springer. [More Information]

2015

  • Chandra, R., Dayal, K. (2015). Coevolutionary recurrent neural networks for prediction of rapid intensification in wind intensity of tropical cyclones in the south pacific region. 22nd International Conference on Neural Information Processing (ICONIP 2015), Cham: Springer. [More Information]
  • Chandra, R. (2015). Competition and Collaboration in Cooperative Coevolution of Elman Recurrent Neural Networks for Time-Series Prediction. IEEE Transactions on Neural Networks and Learning Systems, 26(12), 3123-3136.
  • Bali, K., Chandra, R., Omidvar, M. (2015). Competitive island-based cooperative coevolution for efficient optimization of large-scale fully-separable continuous functions. 22nd International Conference on Neural Information Processing (ICONIP 2015), Cham: Springer. [More Information]
  • Chandra, R., Bali, K. (2015). Competitive two-island cooperative coevolution for real parameter global optimisation. 2015 IEEE Congress on Evolutionary Computation (CEC 2015), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Dayal, K. (2015). Cooperative neuro-evolution of Elman recurrent networks for tropical cyclone wind-intensity prediction in the South Pacific region. 2015 IEEE Congress on Evolutionary Computation (CEC 2015), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Wong, G., Chandra, R. (2015). Enhancing competitive island cooperative neuro-evolution through backpropagation for pattern classification. 22nd International Conference on Neural Information Processing (ICONIP 2015), Cham: Springer. [More Information]
  • Chandra, R., Rolland, L. (2015). Global-Local Population Memetic Algorithm for Solving the Forward Kinematics of Parallel Manipulators. Connection Science, 27(1), 22-39. [More Information]
  • Bali, K., Chandra, R. (2015). Multi-island competitive cooperative coevolution for real parameter global optimization. 22nd International Conference on Neural Information Processing (ICONIP 2015), Cham: Springer. [More Information]
  • Chandra, R. (2015). Multi-objective cooperative neuro-evolution of recurrent neural networks for time series prediction. 2015 IEEE Congress on Evolutionary Computation (CEC 2015), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Nand, R., Chandra, R. (2015). Neuron-synapse level problem decomposition method for cooperative neuro-evolution of feedforward networks for time series prediction. 22nd International Conference on Neural Information Processing (ICONIP 2015), Cham: Springer. [More Information]
  • Bali, K., Chandra, R. (2015). Scaling up multi-island competitive cooperative coevolution for real parameter global optimisation. 28th Australasian Joint Conference on Artificial Intelligence (AI 2015), Cham: Springer. [More Information]
  • Chandra, R., Dayal, K. (2015). Two-Dimensional Time Series Approach for Cyclone Track Prediction using Cooperative Co evolution of Recurrent Neural Networks. 2015 International Joint Conference on Neural Networks (IJCNN 2015), Killarney: Institute of Electrical and Electronics Engineers (IEEE).

2014

  • Chandra, R. (2014). Competitive two-island cooperative coevolution for training Elman recurrent networks for time series prediction. 2014 International Joint Conference on Neural Networks (IJCNN 2014), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chand, S., Chandra, R. (2014). Cooperative coevolution of feed forward neural networks for financial time series problem. 2014 International Joint Conference on Neural Networks (IJCNN 2014), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R. (2014). Memetic cooperative coevolution of Elman recurrent neural networks. Soft Computing, 18(8), 1549-1559. [More Information]
  • Chand, S., Chandra, R. (2014). Multi-objective cooperative coevolution of neural networks for time series prediction. 2014 International Joint Conference on Neural Networks (IJCNN 2014), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Singh, V., Bali, A., Adhikthikar, A., Chandra, R. (2014). Web and mobile based tourist travel guide system for Fiji's tourism industry. 2014 Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE 2014), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

2013

  • Chandra, R. (2013). Adaptation in Cooperative Coevolutionary Recurrent Neural Networks for Time Series Prediction. 2013 International Joint Conference on Neural Networks (IJCNN 2013), Piscataway: Institute of Electrical and Electronics Engineers (IEEE).
  • Chandra, R. (2013). Adaptive problem decomposition in cooperative coevolution of recurrent networks for time series prediction. 2013 International Joint Conference on Neural Networks (IJCNN 2013), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

2012

  • Chandra, R., Frean, M., Zhang, M. (2012). Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks. Soft Computing, 16(6), 1009-1020. [More Information]
  • Chandra, R., Zhang, M., Peng, L. (2012). Application of Cooperative Convolution Optimization for 13C Metabolic Flux Analysis: Simulation of Isotopic Labeling Patterns Based on Tandem Mass Spectrometry Measurements. 9th International Conference on Simulated Evolution and Learning (SEAL 2012), Berlin: Springer. [More Information]
  • Chandra, R., Zhang, M. (2012). Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing, 86, 116-123. [More Information]
  • Chandra, R., Frean, M., Zhang, M. (2012). Crossover-based local search in cooperative co-evolutionary feedforward neural networks. Applied Soft Computing, 12(9), 2924-2932. [More Information]
  • Chandra, R., Frean, M., Zhang, M. (2012). On the issue of separability for problem decomposition in cooperative neuro-evolution. Neurocomputing, 87, 33-40. [More Information]

2011

  • Chandra, R., Frean, M., Zhang, M. (2011). A memetic framework for cooperative coevolution of recurrent neural networks. 2011 International Joint Conference on Neural Networks (IJCNN 2011), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Frean, M., Zhang, M., Omlin, C. (2011). Encoding subcomponents in cooperative co-evolutionary recurrent neural networks. Neurocomputing, 74(17), 3223-3234. [More Information]
  • Chandra, R., Frean, M., Zhang, M. (2011). Modularity adaptation in cooperative coevolution of feedforward neural networks. 2011 International Joint Conference on Neural Networks (IJCNN 2011), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
  • Chandra, R., Rolland, L. (2011). On solving the forward kinematics of 3RPR planar parallel manipulator using hybrid metaheuristics. Applied Mathematics and Computation, 217(22), 8997-9008. [More Information]

2010

  • Chandra, R., Frean, M., Zhang, M. (2010). An Encoding Scheme for Cooperative Co-evolutionary Neural Networks. Proceedings of the 23rd Australasian Joint Conference on Artificial Intelligence.
  • Rolland, L., Chandra, R. (2010). On solving the forward kinematics of the 6-6 General parallel manipulator with an efficient evolutionary algorithm. In Werner Schiehlen, Vincenzo Parenti-Castelli (Eds.), ROMANSY 18 - Robot Design, Dynamics and Control: Proceedings of the Eighteenth CISM-IFToMM Symposium (CISM International Centre for Mechanical Sciences), (pp. 117-124). Springer.

2009

  • Chandra, R., Frean, M., Rolland, L. (2009). A Hybrid Meta-Heuristic Paradigm for Solving the Forward Kinematics of 6-6 General Parallel Manipulator. 8th IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA 2009).
  • Rolland, L., Chandra, R. (2009). Forward Kinematics of the 3RPR planar Parallel Manipulators Using Real Coded Genetic Algorithms. Proceedings of 24th International Symposium on Computer and Information Sciences.
  • Rolland, L., Chandra, R. (2009). Forward Kinematics of the 6-6 general Parallel Manipulator Using Real Coded Genetic Algorithms. Proceedings of 2009 IEEE/ASME Conference on Advanced Intelligent Mechatronics,.
  • Rohitash, C., Knight, R., Omlin, C. (2009). Renosterveld Conservation in South Africa: A Case Study for Handling Uncertainty in Knowledge Based Neural Networks for Environmental Management. Journal of Environmental Informatics, 13(1), 56-65. [More Information]

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