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Dr Raymond Chiong

Lecturer

School of Design Communication and IT (Information Technology)

Career Summary

Biography

Raymond graduated with a PhD degree from the University of Melbourne. He taught various undergraduate and postgraduate courses in the areas of computer science and information systems at Swinburne University of Technology for almost a decade before joining the University of Newcastle Australia in early 2013. Besides teaching, he has been actively pursuing research related to evolutionary game theory, optimisation, data analytics, and modelling of complex adaptive systems. He was the Editor-in-Chief of the Interdisciplinary Journal of Information, Knowledge, and Management from 2011 to 2014. Currently, he is an Editor for the journal Engineering Applications of Artificial Intelligence and an Associate Editor for the IEEE Computational Intelligence Magazine. He also served/is serving as Guest Editors for a number of reputable international journals such as the International Journal of Production Economics and European Journal of Operational Research. He was the Vice Chair of the task force “Education” of IEEE Computational Intelligence Society’s Emergent Technology Technical Committee, and one of the Founding co-Chairs of the IEEE Symposium on Computational Intelligence in Production and Logistics Systems. To date, he has produced/co-authored over 100 refereed publications in the form of books, book chapters, journal articles and conference papers, among others.

Research Expertise
Evolutionary optimisation Evolutionary game theory Technology enhanced learning

Teaching Expertise
Courses coordinated and taught at the University of Newcastle: - INFO6101 Information Technology Research I (postgraduate) - INFO6102 Information Technology Research II (postgraduate) - INFO6030 Systems Analysis and Design (postgraduate) - INFT2012 Applications Programming - EBUS2000 Information and Communication in Business - INFO1010 Introduction to Information Systems and Technology Units convened and taught at Swinburne University of Technology: - MTB610 Data Management and Business Intelligence (postgraduate) - MTB520 Systems Development (postgraduate) - CIS13 Information Systems and Fundamentals - CIS100 Programming Concepts - HIT3181 Technical Software Development - HIT3172 Object-oriented Programming in C++ - HIT2080 Introduction to Programming (in C) - HIT3002 Introduction to Artificial Intelligence - HIT2420 Database Management Systems - HIT2405 Requirement Analysis and Modelling - HIT1402 Database Analysis and Design

Collaborations
Main collaborators: The University of Melbourne - working with Dr Michael Kirley on evolution of (N-player) cooperation using evolutionary computation and agent-based modelling techniques. University of Science and Technology of China - working with Assoc Prof Thomas Weise and his students on intelligent transportation planning and algorithm benchmarking using evolutionary computation methods. Huazhong University of Science and Technology, China - working with Prof Yukun Bao and his team on forecasting of electricity loads and stock prices using machine learning and evolutionary computation methods. Tsinghua University/Nanchang University, China - working with Assoc Prof Rui Zhang and Jianya Ding on sustainable scheduling of production systems using both evolutionary computation and operations research methods.


Keywords

  • Business intelligence
  • Databases
  • Evolutionary computation
  • Evolutionary game theory
  • Machine learning and data mining
  • Modelling of complex systems
  • Programming
  • Systems development

Fields of Research

CodeDescriptionPercentage
080102Artificial Life20
080108Neural, Evolutionary and Fuzzy Computation60
080199Artificial Intelligence and Image Processing not elsewhere classified20

Professional Experience

UON Appointment

DatesTitleOrganisation / Department
1/02/2015 - LecturerUniversity of Newcastle
School of Design Communication and IT
Australia

Academic appointment

DatesTitleOrganisation / Department
1/03/2014 - Guest Research ProfessorHuazhong University of Science And Technology
China
1/07/2005 - 1/01/2013LecturerSwinburne University of Technology, VIC
Australia

Membership

DatesTitleOrganisation / Department
1/01/2005 - Membership - IEEE Institute of Electrical & Electronic Engineers (IEEE)
Australia
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Publications

For publications that are currently unpublished or in-press, details are shown in italics.


Book (4 outputs)

YearCitationAltmetricsLink
2013Chiong R, Weise T, Michalewicz Z, Variants of evolutionary algorithms for real-world applications, Springer-Verlag Berlin Heidelberg (2013)

Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interest from many researchers and practitioners in recent years. This book "Variants of Evolutionary Algorithms for Real-World Applications" aims to promote the practitioner's view on EAs by providing a comprehensive discussion of how EAs can be adapted to the requirements of various applications in the real-world domains. It comprises 14 chapters, including an introductory chapter re-visiting the fundamental question of what an EA is and other chapters addressing a range of real-world problems such as production process planning, inventory system and supply chain network optimisation, task-based jobs assignment, planning for CNC-based work piece construction, mechanical/ship design tasks that involve runtime-intense simulations, data mining for the prediction of soil properties, automated tissue classification for MRI images, and database query optimisation, among others. These chapters demonstrate how different types of problems can be successfully solved using variants of EAs and how the solution approaches are constructed, in a way that can be understood and reproduced with little prior knowledge on optimisation.

DOI10.1007/978-3-642-23424-8
2013Chiong R, Weise T, Michalewicz Z, Preface, Springer-Verlag Berlin Heidelberg (2013)
DOI10.1007/978-3-642-23424-8
2013Jovanovic J, Chiong R, Technological and Social Environments for Interactive Learning, Informing Science Press, Santa Rosa, CA, 492 (2013) [A3]
2009Chiong R, Nature-Inspired Algorithms for Optimisation, Springer-Verlag, Berlin, Germany, 514 (2009) [A3]
Show 1 more book

Chapter (2 outputs)

YearCitationAltmetricsLink
2013Blum C, Chiong R, Clerc M, De Jong K, Michalewicz Z, Neri F, Weise T, 'Evolutionary optimization', Variants of Evolutionary Algorithms for Real-World Applications, Springer-Verlag Berlin Heidelberg 1-29 (2013)

The emergence of different metaheuristics and their new variants in recent years has made the definition of the term Evolutionary Algorithms unclear. Originally, it was coined to put a group of stochastic search algorithms that mimic natural evolution together. While some people would still see it as a specific term devoted to this group of algorithms, including Genetic Algorithms, Genetic Programming, Evolution Strategies, Evolutionary Programming, and to a lesser extent Differential Evolution and Estimation of Distribution Algorithms, many others would regard "Evolutionary Algorithms" as a general term describing population-based search methods that involve some form of randomness and selection. In this chapter, we re-visit the fundamental question of "what is an Evolutionary Algorithm?" not only from the traditional viewpoint but also the wider, more modern perspectives relating it to other areas of Evolutionary Computation. To do so, apart from discussing the main characteristics of this family of algorithms we also look at Memetic Algorithms and the Swarm Intelligence algorithms. From our discussion, we see that establishing semantic borders between these algorithm families is not always easy, nor necessarily useful. It is anticipated that they will further converge as the research from these areas cross-fertilizes each other.

DOI10.1007/978-3-642-23424-8_1
CitationsScopus - 4
2013Moser I, Chiong R, 'Dynamic function optimization: The moving peaks benchmark', Metaheuristics for Dynamic Optimization, Springer, Berlin 35-59 (2013) [B1]
DOI10.1007/978-3-642-30665-5_3
CitationsScopus - 3

Journal article (38 outputs)

YearCitationAltmetricsLink
2015Xiong T, Bao Y, Hu Z, Chiong R, 'Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms', Information Sciences, 305 77-92 (2015)

Interval time series prediction is one of the most challenging research topics in the field of time series modeling and prediction. In view of the remarkable function approximation capability of fully complex-valued radial basis function neural networks (FCRBFNNs), we set out to investigate the possibility of forecasting interval time series by denoting the lower and upper bounds of the interval as real and imaginary parts of a complex number, respectively. This results in a complex-valued interval. We then model the resulted complex-valued interval time series via a FCRBFNN. Furthermore, we propose to evolve the FCRBFNN by using particle swarm optimization (PSO) and discrete PSO for joint optimization of the structure and parameters. Finally, the proposed interval time series prediction approach is tested with simulated interval time series data as well as real interval stock price time series data from the New York Stock Exchange. Our experimental results indicate that it is a promising alternative for interval time series forecasting.

DOI10.1016/j.ins.2015.01.029
2015Weise T, Chiong R, 'An alternative way of presenting statistical test results when evaluating the performance of stochastic approaches', NEUROCOMPUTING, 147 235-238 (2015)
DOI10.1016/j.neucom.2014.06.071Author URL
CitationsWeb of Science - 2
2015Hu Z, Bao Y, Xiong T, Chiong R, 'Hybrid filter-wrapper feature selection for short-term load forecasting', Engineering Applications of Artificial Intelligence, 40 17-27 (2015)

Selection of input features plays an important role in developing models for short-term load forecasting (STLF). Previous studies along this line of research have focused pre-dominantly on filter and wrapper methods. Given the potential value of a hybrid selection scheme that includes both filter and wrapper methods in constructing an appropriate pool of features, coupled with the general lack of success in employing filter or wrapper methods individually, in this study we propose a hybrid filter-wrapper approach for STLF feature selection. This proposed approach, which is believed to have taken full advantage of the strengths of both filter and wrapper methods, first uses the Partial Mutual Information based filter method to filter out most of the irrelevant and redundant features, and subsequently applies a wrapper method, implemented via a firefly algorithm, to further reduce the redundant features without degrading the forecasting accuracy. The well-established support vector regression is selected as the modeler to implement the proposed hybrid feature selection scheme. Real-world electricity load datasets from a North-American electric utility and the Global Energy Forecasting Competition 2012 have been used to test the performance of the proposed approach, and the experimental results show its superiority over selected counterparts.

DOI10.1016/j.engappai.2014.12.014
CitationsScopus - 1Web of Science - 1
2015Li B, Chiong R, Lin M, 'A balance-evolution artificial bee colony algorithm for protein structure optimization based on a three-dimensional AB off-lattice model', Computational Biology and Chemistry, 54 1-12 (2015) [C1]

Protein structure prediction is a fundamental issue in the field of computational molecular biology. In this paper, the AB off-lattice model is adopted to transform the original protein structure prediction scheme into a numerical optimization problem. We present a balance-evolution artificial bee colony (BE-ABC) algorithm to address the problem, with the aim of finding the structure for a given protein sequence with the minimal free-energy value. This is achieved through the use of convergence information during the optimization process to adaptively manipulate the search intensity. Besides that, an overall degradation procedure is introduced as part of the BE-ABC algorithm to prevent premature convergence. Comprehensive simulation experiments based on the well-known artificial Fibonacci sequence set and several real sequences from the database of Protein Data Bank have been carried out to compare the performance of BE-ABC against other algorithms. Our numerical results show that the BE-ABC algorithm is able to outperform many state-of-the-art approaches and can be effectively employed for protein structure optimization.

DOI10.1016/j.compbiolchem.2014.11.004
2015Weise T, Chiong R, 'An alternative way of presenting statistical test results when evaluating the performance of stochastic approaches', Neurocomputing, 147 235-238 (2015)

Stochastic approaches such as evolutionary algorithms have been widely used in various science and engineering problems. When comparing the performance of a set of stochastic algorithms, it is necessary to statistically evaluate which algorithms are the most suitable for solving a given problem. The outcome of statistical tests comparing N=2 processes, where N is the number of algorithms, is often presented in tables. This can become confusing for larger numbers of N. Such a scenario is, however, very common in both numerical and combinatorial optimization as well as in the domain of stochastic algorithms in general. In this letter, we introduce an alternative way of visually presenting the results of statistical tests for multiple processes in a compact and easy-to-read manner using a directed acyclic graph (DAG), in the form of a simplified Hasse diagram. The rationale of doing so is based on the fact that the outcome of the tests is always at least a strict partial order, which can be appropriately presented via a DAG. The goal of this brief communication is to promote the use of this approach as a means for presenting the results of comparisons between different optimization methods.

DOI10.1016/j.neucom.2014.06.071
CitationsScopus - 2
2015Ding J-Y, Song S, Gupta JND, Zhang R, Chiong R, Wu C, 'An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem', Applied Soft Computing Journal, 30 604-613 (2015)

This paper proposes a Tabu-mechanism improved iterated greedy (TMIIG) algorithm to solve the no-wait flowshop scheduling problem with a makespan criterion. The idea of seeking further improvement in the iterated greedy (IG) algorithm framework is based on the observation that the construction phase of the original IG algorithm may not achieve good performance in escaping from local minima when incorporating the insertion neighborhood search. To overcome this limitation, we have modified the IG algorithm by utilizing a Tabu-based reconstruction strategy to enhance its exploration ability. A powerful neighborhood search method that involves insert, swap, and double-insert moves is then applied to obtain better solutions from the reconstructed solution in the previous step. Empirical results on several benchmark problem instances and those generated randomly confirm the advantages of utilizing the new reconstruction scheme. In addition, our results also show that the proposed TMIIG algorithm is relatively more effective in minimizing the makespan than other existing well-performing heuristic algorithms.

DOI10.1016/j.asoc.2015.02.006
2015Hu Z, Bao Y, Chiong R, Xiong T, 'Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection', Energy, 84 419-431 (2015)

Accurate forecasting of mid-term electricity load is an important issue for power system planning and operation. Instead of point load forecasting, this study aims to model and forecast mid-term interval loads up to one month in the form of interval-valued series consisting of both peak and valley points by using MSVR (Multi-output Support Vector Regression). In addition, an MA (Memetic Algorithm) based on the firefly algorithm is used to select proper input features among the feature candidates, which include time lagged loads as well as temperatures. The capability of this proposed interval load modeling and forecasting framework to predict daily interval electricity demands is tested through simulation experiments using real-world data from North America and Australia. Quantitative and comprehensive assessments are performed and the experimental results show that the proposed MSVR-MA forecasting framework may be a promising alternative for interval load forecasting.

DOI10.1016/j.energy.2015.03.054
2015Li B, Chiong R, Lin M, 'A balance-evolution artificial bee colony algorithm for protein structure optimization based on a three-dimensional AB off-lattice model', COMPUTATIONAL BIOLOGY AND CHEMISTRY, 54 1-12 (2015) [C1]
DOI10.1016/j.compbiolchem.2014.11.004Author URL
2015Lo SL, Chiong R, Cornforth D, 'Using Support Vector Machine Ensembles for Target Audience Classification on Twitter', PLOS ONE, 10 (2015)
DOI10.1371/journal.pone.0122855Author URL
Co-authorsDavid Cornforth
2015Lo SL, Chiong R, Cornforth D, 'Using support vector machine ensembles for target audience classification on twitter.', PloS one, 10 e0122855 (2015)
DOI10.1371/journal.pone.0122855
Co-authorsDavid Cornforth
2014Catay B, Chiong R, Cordón O, Siarry P, 'Computational intelligence in production and logistics systems: Solving vehicle routing, supply chain network, and air-traffic trajectory planning problems [guest editorial]', IEEE Computational Intelligence Magazine, 9 16-17 (2014) [C3]
DOI10.1109/MCI.2014.2350932
2014Weise T, Chiong R, Lassig J, Tang K, Tsutsui S, Chen W, et al., 'Benchmarking optimization algorithms: An open source framework for the traveling salesman problem', IEEE Computational Intelligence Magazine, 9 40-52 (2014) [C1]
DOI10.1109/MCI.2014.2326101
CitationsScopus - 4Web of Science - 2
2014Hu Z, Bao Y, Chiong R, Xiong T, 'Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection', Energy, (2014)

Accurate forecasting of mid-term electricity load is an important issue for power system planning and operation. Instead of point load forecasting, this study aims to model and forecast mid-term interval loads up to one month in the form of interval-valued series consisting of both peak and valley points by using MSVR (Multi-output Support Vector Regression). In addition, an MA (Memetic Algorithm) based on the firefly algorithm is used to select proper input features among the feature candidates, which include time lagged loads as well as temperatures. The capability of this proposed interval load modeling and forecasting framework to predict daily interval electricity demands is tested through simulation experiments using real-world data from North America and Australia. Quantitative and comprehensive assessments are performed and the experimental results show that the proposed MSVR-MA forecasting framework may be a promising alternative for interval load forecasting.

DOI10.1016/j.energy.2015.03.054
2013Catay B, Chiong R, Siarry P, 'Computational intelligence in production and logistics systems', International Journal Production Economics, 145 1-3 (2013) [C3]
Author URL
CitationsScopus - 2Web of Science - 1
2013Abedini M, Kirley M, Chiong R, 'Incorporating feature ranking and evolutionary methods for the classification of high-dimensional DNA microarray gene expression data', Australasian Medical Journal, 6 272-279 (2013) [C1]

Background: DNA microarray gene expression classification poses a challenging task to the machine learning domain. Typically, the dimensionality of gene expression data sets could go from several thousands to over 10,000 genes. A potential solution to this issue is using feature selection to reduce the dimensionality. Aim The aim of this paper is to investigate how we can use feature quality information to improve the precision of microarray gene expression classification tasks. Method: We propose two evolutionary machine learning models based on the eXtended Classifier System (XCS) and a typical feature selection methodology. The first one, which we call FS-XCS, uses feature selection for feature reduction purposes. The second model is GRD-XCS, which uses feature ranking to bias the rule discovery process of XCS. Results: The results indicate that the use of feature selection/ranking methods is essential for tackling high-dimensional classification tasks, such as microarray gene expression classification. However, the results also suggest that using feature ranking to bias the rule discovery process performs significantly better than using the feature reduction method. In other words, using feature quality information to develop a smarter learning procedure is more efficient than reducing the feature set. Conclusion: Our findings have shown that extracting feature quality information can assist the learning process and improve classification accuracy. On the other hand, relying exclusively on the feature quality information might potentially decrease the classification performance (e.g., using feature reduction). Therefore, we recommend a hybrid approach that uses feature quality information to direct the learning process by highlighting the more informative features, but at the same time not restricting the learning process to explore other features.

DOI10.4066/AMJ.2013.1641
CitationsScopus - 2
2012Chiong R, Kirley M, 'Random Mobility and the Evolution of Cooperation in Spatial N-player Iterated Prisoner's Dilemma Games', Physica A: Statistical Mechanics and its Applications, 391 3915-3923 (2012) [C1]
DOI10.1016/j.physa.2012.03.010
CitationsScopus - 5Web of Science - 3
2012Chiong R, Siarry P, 'Local search for real-world scheduling and planning', ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 25 207-208 (2012) [C3]
DOI10.1016/j.engappai.2011.07.008Author URL
CitationsScopus - 1Web of Science - 1
2012Tang K, Weise T, Chiong R, 'Evolutionary Optimization: Pitfalls and Booby Traps', Journal of Computer Science and Technology, 27 907-936 (2012) [C1]
DOI10.1007/s11390-012-1274-4
CitationsScopus - 15Web of Science - 11
2012Chiong R, Kirley M, 'Effects of Iterated Interactions in Multi-player Spatial Evolutionary Games', IEEE Transactions on Evolutionary Computation, 16 537-555 (2012) [C1]
DOI10.1109/TEVC.2011.2167682
CitationsScopus - 11Web of Science - 6
2012Chiong R, Jovanovic J, 'Collaborative Learning in Online Study Groups: An Evolutionary Game Theory Perspective', Journal of Information Technology Education, 11 81-101 (2012) [C1]
CitationsScopus - 6
2012Jovanovic J, Weise T, Chiong R, 'Social networking, teaching, and learning', Interdisciplinary Journal of Information, Knowledge, and Management, 7 39-43 (2012) [C3]
2012Jovanovic J, Chiong R, 'Introduction to the special section on game-based learning: Design and applications', Interdisciplinary Journal of Information, Knowledge, and Management, 7 201-203 (2012) [C3]
2011Su SI, Chiong R, 'Adaptive business intelligence for information and communication technology management 139-156 (2011)

With the rapid advancement of both business techniques and technologies in recent years, knowledge has become an important and strategic asset that determines the success or failure of an organisation. Analyses showed that a competitive advantage in business environment depends on the accessibility to adequate and reliable information in shortest time possible and the high selectivity in the creation and utilisation of information. An effective instrument to create, aggregate and share knowledge in an organisation has therefore become a key target of management. As the organisations today can no longer spend money on excessive infrastructure and technology that does not provide a quick impact on the business' bottom-line, Business Intelligence (BI) is becoming indispensable to an organistion's success in the emerging global economy. BI is currently one of the fastest developing directions in information and communication technology. In this paper, we introduce the concept of BI and address the importance of BI in revolutionising knowledge towards economics and business advancement. Ensuing sections then explore some specific BI applications, including discussion surrounding the technologies and frameworks which may facilitate BI in an e-business initiative. In the concluding remarks, the future of BI is drawn to wrap up the studies. © 2011 by Nova Science Publishers, Inc. All rights reserved.

2011Chiong R, Weise T, 'Special issue on modern search heuristics and applications', Evolutionary Intelligence, 4 1-2 (2011) [C3]
DOI10.1007/s12065-011-0050-7
CitationsScopus - 2
2011Pu W, Weise T, Chiong R, 'Novel Evolutionary Algorithms for Supervised Classification Problems: An Experimental Study', Evolutionary Intelligence, 4 3-16 (2011) [C1]
DOI10.1007/s12065-010-0047-7
CitationsScopus - 8
2011Weise T, Chiong R, 'A Novel Extremal Optimization Approach for the Template Design Problem', IJOCI, 2 1-16 (2011) [C1]
DOI10.4018/joci.2011040101
2010Moser I, Chiong R, 'Dynamic function optimisation with hybridised extremal dynamics', Memetic Computing, 2 137-148 (2010) [C1]

Dynamic function optimisation is an important research area because many real-world problems are inherently dynamic in nature. Over the years, a wide variety of algorithms have been proposed to solve dynamic optimisation problems, and many of these algorithms have used the Moving Peaks (MP) benchmark to test their own capabilities against other approaches. This paper presents a detailed account of our hybridised Extremal Optimisation (EO) approach that has achieved hitherto unsurpassed results on the three standardised scenarios of the MP problem. Several different components are used in the hybrid EO, and it has been shown that a large proportion of the quality of its outstanding performance is due to the local search component. In this paper, the behaviour of the local search algorithms used is analysed, and the roles of other components are discussed. In the concluding remarks, the generalisation ability of this method and its wider applicability are highlighted. © Springer-Verlag 2009.

DOI10.1007/s12293-009-0027-6
CitationsScopus - 10
2010Su SI, Chiong R, 'Business intelligence 72-80 (2010)
DOI10.4018/978-1-59904-931-1.ch008
2009Weise T, Zapf M, Chiong R, Nebro AJ, 'Why is optimization difficult?', Studies in Computational Intelligence, 193 1-50 (2009) [C3]

This chapter aims to address some of the fundamental issues that are often encountered in optimization problems, making them difficult to solve. These issues include premature convergence, ruggedness, causality, deceptiveness, neutrality, epistasis, robustness, overfitting, oversimplification, multi-objectivity, dynamic fitness, the No Free Lunch Theorem, etc. We explain why these issues make optimization problems hard to solve and present some possible countermeasures for dealing with them. By doing this, we hope to help both practitioners and fellow researchers to create more efficient optimization applications and novel algorithms. © 2009 Springer-Verlag Berlin Heidelberg.

DOI10.1007/978-3-642-00267-0_1
CitationsScopus - 14
2009Weise T, Chiong R, 'Evolutionary approaches and their applications to distributed systems 114-149 (2009)

The ubiquitous presence of distributed systems has drastically changed the way the world interacts, and impacted not only the economics and governance but also the society at large. It is therefore important for the architecture and infrastructure within the distributed environment to be continuously renewed in order to cope with the rapid changes driven by the innovative technologies. However, many problems in distributed computing are either of dynamic nature, large scale, NP complete, or a combination of any of these. In most cases, exact solutions are hardly found. As a result, a number of intelligent nature-inspired algorithms have been used recently, as these algorithms are capable of achieving good quality solutions in reasonable computational time. Among all the nature-inspired algorithms, evolutionary algorithms are considerably the most extensively applied ones. This chapter presents a systematic review of evolutionary algorithms employed to solve various problems related to distributed systems. The review is aimed at providing an insight of evolutionary approaches, in particular genetic algorithms and genetic programming, in solving problems in five different areas of network optimization: network topology, routing, protocol synthesis, network security, and parameter settings and configuration. Some interesting applications from these areas will be discussed in detail with the use of illustrative examples. © 2010, IGI Global.

DOI10.4018/978-1-60566-798-0.ch006
CitationsScopus - 2
2009Chiong R, Neri F, McKay RI, 'Nature that breeds solutions 1-24 (2009)

Nature has always been a source of inspiration. Over the last few decades, it has stimulated many successful techniques, algorithms and computational applications for dealing with large, complex and dynamic real world problems. In this chapter, the authors discuss why nature-inspired solutions have become increasingly important and favourable for tackling the conventionally-hard problems. They also present the concepts and background of some selected examples from the domain of natural computing, and describe their key applications in business, science and engineering. Finally, the future trends are highlighted to provide a vision for the potential growth of this field. © 2010, IGI Global.

DOI10.4018/978-1-60566-705-8.ch001
CitationsScopus - 7
2009Chiong R, 'Intelligent systems for automated learning and adaptation: Emerging trends and applications', Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications, 1-339 (2009)

Intelligent systems are rapidly becoming a central focus of study for researchers as they have the ability to learn and adapt during their existence in order to achieve certain goals and objectives. Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications presents methodologies, architectures, and approaches on experiential automated learning. This critical mass of sought after research assembles the most intriguing and innovative applications, advancements, and leading studies on the methodology of intellectual computing. © 2010 by IGI Global. All rights reserved.

DOI10.4018/978-1-60566-798-0
2009Chiong R, 'Nature-inspired informatics for intelligent applications and knowledge discovery: Implications in business, science, and engineering', Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering, 1-427 (2009)

Recently, nature has stimulated many successful techniques, algorithms, and computational applications allowing conventionally difficult problems to be solved through novel computing systems. Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering provides the latest findings in nature-inspired algorithms and their applications for breakthroughs in a wide range of disciplinary fields. This defining reference collection contains chapters written by leading researchers and well-known academicians within the field, offering readers a valuable and enriched accumulation of knowledge. © 2009 by IGI Global. All rights reserved.

DOI10.4018/978-1-60566-705-8
2009Chiong R, 'Preface', Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering, (2009)
DOI10.4018/978-1-60566-705-8
2008Chiong R, 'Evolving cooperative agents in economy market using genetic algorithms', Studies in Computational Intelligence, 92 313-326 (2008) [C2]

This chapter seeks to follow Axelrod's research of computer simulations on the Iterated Prisoner's Dilemma (IPD) game to investigate the use of Genetic Algorithms (GA) in evolving cooperation within a competitive market environment. We use an agent-based economy model as the basis of our experiments to examine how well GA could perform against the IPD game strategies. We also explore the strategic interactions among the agents that represent firms in a coevolving population, and study the influence of the genetic operators on GA to evolve cooperative agents. © 2008 Springer-Verlag Berlin Heidelberg.

DOI10.1007/978-3-540-76286-7_14
2008Chiong R, Jankovic L, 'Agent strategies in economy market 1-33 (2008)

This chapter presents a method on modelling the economy market using agent-based representation and iterated prisoner's dilemma (IPD). While IPD has been used widely in various economic problems, most of the studies were based on quantitative data which could be deductive and inappropriate. The main objective of this chapter is to present a unique agent-based approach which places lower demand on data using IPD to model the complexity of the economy market. We create a simulated market environment with agents acting as firms to perform transactions among each other with chosen IPD strategy. From empirical results, we investigate strategic interactions among different firms. In the concluding remarks, we present our observations on the qualities of a winning strategy. © 2008, IGI Global.

DOI10.4018/978-1-59904-962-5.ch001
2008Chiong R, Jankovic L, 'Learning game strategy design through iterated Prisoner's Dilemma', International Journal of Computer Applications in Technology, 32 216-223 (2008) [C1]

The article investigates games strategies on the basis of experiments with iterated Prisoner's Dilemma, a classical non-zero sum game. The objective is to determine which strategies have the best chance of winning. Although some strategies, like tit-for-tat, emerge as better than others in some cases, it appears that there is no overall winning strategy, but that success or failure of individual strategies depends upon the strategies adopted by a population of opponents. Therefore, the winning strategy will change dynamically, and will need to be determined while the game in progress. Based on the results of this work, a strategy engine for games development is proposed, and a future development of strategy middleware is discussed. Copyright © 2008 Inderscience Enterprises Ltd.

DOI10.1504/IJCAT.2008.020957
2007Issac B, Mering J, Chiong R, Jacob SM, Then P, 'E-learning implementation and its diverse effects 260-277 (2007)

The rapid growth of technological advances in recent years has opened a completely new dimension to progress in education and training. The emergence of e-learning has created not only business and educational opportunities, but also significantly improved the standard of the society. This chapter explores the implementation of e-learning and its impact on a community, similar to a university or corporate setup. To this aim, a brief introduction into e-learning technology and an example of using the Blackboard Learning System are brought forth, along with some critical success factors. Projecting the e-learning advantages along with the digital library concepts, the economic benefits of such implementation are highlighted. The discussion then moves to the perspective of students and teachers on e-learning. As the trend in the technological world is moving toward mobility, the wireless e-learning perception is also conferred. In the concluding remarks, e-learning implementation is noted as a positive endeavor to boost economic growth. © 2008, IGI Global.

DOI10.4018/978-1-59904-579-5.ch019
Show 35 more journal articles

Conference (54 outputs)

YearCitationAltmetricsLink
2015Jiang Y, Weise T, Lassig J, Chiong R, Athauda R, 'Comparing a hybrid branch and bound algorithm with evolutionary computation methods, local search and their hybrids on the TSP', IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIPLS 2014: 2014 IEEE Symposium on Computational Intelligence in Production and Logistics Systems, Proceedings (2015)

Benchmarking is one of the most important ways to investigate the performance of metaheuristic optimization algorithms. Yet, most experimental algorithm evaluations in the literature limit themselves to simple statistics for comparing end results. Furthermore, comparisons between algorithms from different 'families' are rare. In this study, we use the TSP Suite - an open source software framework - to investigate the performance of the Branch and Bound (BB) algorithm for the Traveling Salesman Problem (TSP). We compare this BB algorithm to an Evolutionary Algorithm (EA), an Ant Colony Optimization (ACO) approach, as well as three different Local Search (LS) algorithms. Our comparisons are based on a variety of different performance measures and statistics computed over the entire optimization process. The experimental results show that the BB algorithm performs well on very small TSP instances, but is not a good choice for any medium to large-scale problem instances. Subsequently, we investigate whether hybridizing BB with LS would give rise to similar positive results like the hybrid versions of EA and ACO have. This turns out to be true - the 'Memetic' BB algorithms are able to improve the performance of pure BB algorithms significantly. It is worth pointing out that, while the results presented in this paper are consistent with previous findings in the literature, our results have been obtained through a much more comprehensive and solid experimental procedure.

DOI10.1109/CIPLS.2014.7007174
Co-authorsRukshan Athauda
2015Li B, Chiong R, Lin M, 'A two-layer optimization framework for UAV path planning with interval uncertainties', IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIPLS 2014: 2014 IEEE Symposium on Computational Intelligence in Production and Logistics Systems, Proceedings (2015)

We propose a two-layer optimization framework for the unmanned aerial vehicle path planning problem to handle interval uncertainties that exist in the combat field. When evaluating a candidate flight path, we first calculate the interval response (i.e., the upper and lower bounds) of the candidate flight path within the inner layer of the framework using a collocation interval analysis method (CIAM). Then, in the outer layer, we introduce a novel criterion for interval response comparison. The artificial bee colony algorithm is used to search for the optimal flight path according to this new criterion. Our experimental results show that the CIAM adopted is a feasible option, which largely eases the computational burden. Moreover, our derived flight paths can effectively handle bounded uncertainties without knowing the corresponding uncertainty distributions.

DOI10.1109/CIPLS.2014.7007170
2015Alharbi NM, Athauda RI, Chiong R, 'A survey of CSCL script tools that support designing collaborative scenarios', 2014 International Conference on Web and Open Access to Learning, ICWOAL 2014 (2015) [E1]

Collaborative learning is proven to be effective in improving cognitive skills (e.g., critical thinking) and acquiring knowledge through group interactions and activities. An active research field known as Computer Supported Collaborative Learning (CSCL) aims to enrich collaborative learning using technology. These days, online learning environments equipped with various communication tools (e.g., chat sessions, discussion forums, voting, video conferencing, etc.) are widely used by many higher education institutions. However, the CSCL research community has identified potential issues among online learners, one of which is the lack of productive collaboration in online learning environments. To address these issues, CSCL researchers suggest the use of CSCL scripts to trigger and structure collaborative learning activities. This paper provides a review of current tools that have been developed and applied to design CSCL scripts in order to enhance collaborative learning.

DOI10.1109/ICWOAL.2014.7009226
Co-authorsRukshan Athauda
2015Lo SL, Cornforth D, Chiong R, 'Use of a High-Value Social Audience index for target audience identification on Twitter', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2015)

With the large and growing user base of social media, it is not an easy feat to identify potential customers for business. This is mainly due to the challenge of extracting commercially viable contents from the vast amount of free-form conversations. In this paper, we analyse the Twitter content of an account owner and its list of followers through various text mining methods and segment the list of followers via an index. We have termed this index as the High-Value Social Audience (HVSA) index. This HVSA index enables a company or organisation to devise their marketing and engagement plan according to available resources, so that a high-value social audience can potentially be transformed to customers, and hence improve the return on investment.

Co-authorsDavid Cornforth
2015Cornforth DJ, Lo SL, chiong R, 'Identifying the high-value social audience from Twitter through text-mining methods', Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Singapore (2015)
Co-authorsDavid Cornforth
2015Lo SL, Cornforth DJ, Chiong R, 'Effects of training datasets on both the Extreme Learning Machine and Support Vector Machine for target Audience Identification on Twitter', Proceedings of ELM-2014 Volume 1, Singapore (2015)
DOI10.1007/978-3-319-14063-6_35
Co-authorsDavid Cornforth
2014Li B, Chiong R, Gong L-G, 'Search-evasion path planning for submarines using the Artificial Bee Colony algorithm', Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (2014) [E1]

Submarine search-evasion path planning aims to acquire an evading route for a submarine so as to avoid the detection of hostile anti-submarine searchers such as helicopters, aircraft and surface ships. In this paper, we propose a numerical optimization model of search-evasion path planning for invading submarines. We use the Artificial Bee Colony (ABC) algorithm, which has been confirmed to be competitive compared to many other nature-inspired algorithms, to solve this numerical optimization problem. In this work, several search-evasion cases in the two-dimensional plane have been carefully studied, in which the anti-submarine vehicles are equipped with sensors with circular footprints that allow them to detect invading submarines within certain radii. An invading submarine is assumed to be able to acquire the real-time locations of all the anti-submarine searchers in the combat field. Our simulation results show the efficacy of our proposed dynamic route optimization model for the submarine search-evasion path planning mission.

DOI10.1109/CEC.2014.6900224
CitationsScopus - 2
2014Alharbi N, Athauda R, Chiong R, 'A Survey of CSCL Script Tools that Support Designing Collaborative Scenarios', 2014 International Conference on Web and Open Access to Learning Conference, Dubai, UAE (2014) [E1]
DOI10.1109/ICWOAL.2014.7009226
Co-authorsRukshan Athauda
2014Li B, Chiong R, Lin M, 'A two-layer optimization framework for UAV path planning with interval uncertainties', IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIPLS 2014: 2014 IEEE Symposium on Computational Intelligence in Production and Logistics Systems, Proceedings, Orlando, Florida (2014) [E1]
DOI10.1109/CIPLS.2014.7007170
2014Jiang Y, Weise T, Lassig J, Chiong R, Athauda R, 'Comparing a hybrid branch and bound algorithm with evolutionary computation methods, local search and their hybrids on the TSP', IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIPLS 2014: 2014 IEEE Symposium on Computational Intelligence in Production and Logistics Systems, Proceedings, Orlando, Florida (2014) [E1]
DOI10.1109/CIPLS.2014.7007174
Co-authorsRukshan Athauda
2014Alharbi N, Athauda R, Chiong R, 'An Integrated Framework for Collaboration in Online Learning Using CSCL Scripts', The 9th International Conference on Information Technology and Application (ICITA 2014), Sydney, Australia (2014) [E1]
Co-authorsRukshan Athauda
2014Li X, Tian X, Chiong RJW, 'Provenancing qualifications in higher education: An Australian-Chinese case study', Proceedings of Informing Science & IT Education Conference (InSITE 2014), Wollongong (2014) [E1]
2013Abedini M, Kirley M, Chiong R, Weise T, 'GPU-accelerated eXtended Classifier System', 2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING (CIDM), Singapore, SINGAPORE (2013) [E1]
Author URL
2013Ouyang J, Weise T, Devert A, Chiong RJW, 'SDGP: A Developmental Approach for Traveling Salesman Problems', Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS), Singapore (2013) [E1]
DOI10.1109/CIPLS.2013.6595203
2013Chiong RJW, Kirley M, 'A multi-agent based migration model for evolving cooperation in the spatial N-player Snowdrift game', Prima 2013 Principles and Practice of Multi-Agent Systems 16th International Conference Proceedings, Dunedin, New Zealand (2013) [E1]
DOI10.1007/978-3-642-44927-7
2012Chiong R, Kirley M, 'The evolution of cooperation via stigmergic interactions', Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2012), Brisbane, Australia (2012) [E1]
2012Abedini M, Kirley M, Chiong R, 'FS-XCS vs. GRD-XCS: An Analysis Using High-dimensional DNA Microarray Gene Expression Data Sets', Proceedings of the 2nd Australian Workshop on Artificial Intelligence in Health (AIH 2012), Sydney, Australia (2012) [E1]
2011Chiong R, Kirley M, 'Iterated N-Player games on small-world networks', Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2011, Dublin, Ireland (2011) [E1]
CitationsScopus - 6
2011Weise T, Niemczyk S, Chiong R, Wan M, 'A framework for multi-model EDAs with model recombination', Proceedings of the European Conference on the Applications of Evolutionary Computation (EvoApplications 2011), Torino, Italy (2011) [E1]
CitationsScopus - 1
2010Chiong R, Kjirley M, 'Co-evolution of agent strategies in N-player dilemmas', Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada (2010) [E1]
2010Weise T, Chiong R, 'Evolutionary data mining approaches for rule-based and tree-based classifiers', Proceedings of the 9th IEEE International Conference on Cognitive Informatics (ICCI 2010), Beijing, China (2010) [E1]
CitationsScopus - 1
2010Chiong R, Kirley M, 'Imitation vs evolution: analysing the effects of strategy update mechanisms in N-player social dilemmas', Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2010), Barcelona, Spain (2010) [E1]
2010Chiong R, Kirley M, 'Evolving cooperation in the spatial N-player snowdrift game', Proceedings of the 23rd Australasian Joint Conference on Artificial Intelligence (AI 2010), Adelaide, Australia (2010) [E1]
CitationsScopus - 2Web of Science - 2
2009Chiong R, Weise T, Lau BT, 'Template design using Extremal Optimization with multiple search operators', Proceedings of the International Conference on Soft Computing and Pattern Recognition (SoCPaR 2009), Malacca, Malaysia (2009) [E1]
2009Lau BT, Thai TC, Chiong R, 'An intelligent real-time communication assistant for the disabled', Proceedings of the International Conference on Soft Computing and Pattern Recognition (SoCPaR 2009), Malacca, Malaysia (2009) [E1]
2009Moser I, Chiong R, 'A hooke-jeeves based memetic algorithm for solving dynamic optimisation problems', Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2009), Salamanca, Spain (2009) [E1]
CitationsScopus - 2Web of Science - 3
2009Chiong R, Kirley M, 'Co-evolutionary learning in the N-player iterated prisoner's dilemma with a structured environment', Proceedings of the 4th Australian Conference on Artificial Life (ACAL 2009), Melbourne, Australia (2009) [E1]
CitationsWeb of Science - 1
2009Dhakal S, Chiong R, Lau BT, 'A comparative study of tree-based search algorithms on the London Underground', Proceedings of the 1st Malaysian Joint Conference on Artificial Intelligence (MJCAI 2009), Kuala Lumpur, Malaysia (2009) [E1]
2009Chiong R, Dhakal S, Lau BT, 'Solving the examination timetabling problem with controlled avalanche of changes', Proceedings of the 6th International Conference on Information Technology in Asia (CITA 2009), Kuching, Malaysia (2009) [E1]
2008Chiong R, Dhakal S, 'Modelling database security through agent-based simulation', Proceedings of the 2nd Asia International Conference on Modelling and Simulation (AMS 2008), Kuala Lumpur, Malaysia (2008) [E1]
CitationsScopus - 2
2008Chang YY, Yung S, Chiong R, 'A novel approach for intelligent route finding through cumulative proximity evaluation', Proceedings of the 2nd Asia International Conference on Modelling and Simulation (AMS 2008), Kuala Lumpur, Malaysia (2008) [E1]
2008Jap WJ, Sutanto JH, Chiong R, 'An implementation of ant colony optimisation for solving cutting stock problem', Proceedings of the 4th IASTED International Conference on Advances in Computer Science and Technology (ACST 2008), Langkawi, Malaysia (2008) [E1]
2008Chiong R, Lau BT, 'A hybrid Naive Bayes approach for information filtering', Proceedings of the 3rd IEEE Conference on Industrial Electronics and Applications (ICIEA 2008), Singapore, Singapore (2008) [E1]
CitationsScopus - 1
2008Chiong R, Chang YY, Chai PC, Wong AL, 'A selective mutation based evolutionary programming for solving cutting stock problem without contiguity', Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2008), Hong Kong (2008) [E1]
CitationsScopus - 2Web of Science - 1
2008Dhakal S, Chiong R, 'A hybrid nearest neighbour and progressive improvement approach for travelling salesman problem', Proceedings of the 3rd International Symposium on Information Technology (ITSIM 2008), Kuala Lumpur, Malaysia (2008) [E1]
2008Chiong R, Sutanto JH, Jap WJ, 'A comparative study on informed and uninformed search for intelligent travel planning in Borneo Island', Proceedings of the 3rd International Symposium on Information Technology (ITSIM 2008), Kuala Lumpur, Malaysia (2008) [E1]
CitationsScopus - 1Web of Science - 1
2008Chiong R, Dhakal S, 'On the insecurity of personal firewall', Proceedings of the 3rd International Symposium on Information Technology (ITSIM 2008), Kuala Lumpur, Malaysia (2008) [E1]
2007Chiong R, Lee MH, Tham CM, 'The implications of Web 2.0 on the delivery of e-learning', Proceedings of the UiTM International Conference on E-Learning (UiCEL 2007), Shah Alam, Malaysia (2007) [E1]
2007Chiong R, 'Applying Genetic Algorithms to Economy Market using Iterated Prisoner's Dilemma', APPLIED COMPUTING 2007, VOL 1 AND 2, Seoul, SOUTH KOREA (2007) [E1]
Author URL
CitationsScopus - 4
2007Chiong R, 'Modelling agent strategies in simulated market using iterated prisoner's dilemma', COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2007, PT 3, PROCEEDINGS, Kuala Lumpur, MALAYSIA (2007) [E1]
Author URL
2007Chiong R, Dhakal S, Jankovic L, 'Effects of neighbourhood structure on evolution of cooperation in N-Player Iterated Prisoner's Dilemma', INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2007, Birmingham, ENGLAND (2007) [E1]
Author URL
CitationsScopus - 3Web of Science - 5
2007Theng LB, Chiong R, 'An improved snake for automatic building extraction', CIMMACS '07: PROCEEDINGS OF THE 6TH WSEAS INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS, Puerto de la Cruz, SPAIN (2007) [E1]
Author URL
2007Suai W, Chiong R, 'A comparison of the security features of .NET and J2EE', Proceedings of the 2nd International Conference on Informatics (Informatics 2007), Kuala Lumpur, Malaysia (2007) [E1]
2007Chiong R, 'A hybrid classification approach for content-based email filtering', Proceedings of the 5th International Conference on Information Technology in Asia (CITA 2007), Kuching, Malaysia (2007) [E1]
2007Chiong R, '22nd Annual ACM Symposium on Applied Computing', Proceedings of the International Conference on Information Technology and Management (ICITM 2007), Hong Kong (2007) [E1]
2006Issac B, Chiong R, Jacob SM, 'Analysis of phishing attacks and countermeasures', Managing Information in the Digital Economy: Issues & Solutions, Bonn, GERMANY (2006) [E1]
Author URL
2006Chiong R, Wong DML, Jankovic L, 'Agent-Based Economic Modelling with Iterated Prisoner's Dilemma', 2006 INTERNATIONAL CONFERENCE ON COMPUTING & INFORMATICS (ICOCI 2006), Kuala Lumpur, MALAYSIA (2006)
Author URL
2006Chiong R, Wei W, 'Named entity recognition using hybrid machine learning approach', Proceedings of the Fifth IEEE International Conference on Cognitive Informatics, Vols 1 and 2, Beijing, PEOPLES R CHINA (2006) [E1]
Author URL
CitationsScopus - 1
2006Chiong R, Wong DML, Jankovic L, 'Agent-based economic modelling with iterated prisoner's dilemma', 2006 International Conference on Computing and Informatics, ICOCI '06 (2006) [E1]

This paper presents a method on modelling the economy market using agent-based representation and iterated prisoner's dilemma (IPD). While IPD has been widely used in various problems, there is little work done in modelling the dynamics of market behaviour and it is the main objective of this work to fill in this gap. We create a simulated market environment with agents acting as firms to perform transactions among each other with chosen IPD strategy. From the empirical results, we investigate the strategic interactions among different firms in relation to real-world transaction scenario. In the concluding remarks, we present our observation on the qualities of a winning strategy. ©2006 IEEE.

DOI10.1109/ICOCI.2006.5276488
2006Chiong R, Wong DM, 'Survey on the applications of artificial neural networks in computer games', Proceedings of the 3rd International Conference on Artificial Intelligence in Engineering and Technology (ICAIET 2006), Kota Kinabalu, Malaysia (2006) [E1]
2006Chiong R, 'An improved evolutionary approach for solving one-dimensional cutting stock problem', Proceedings of the 3rd International Conference on Artificial Intelligence in Engineering and Technology (ICAIET 2006), Kota Kinabalu, Malaysia (2006) [E1]
2006Chiong R, Jankovic L, 'Genetic algorithm as a game strategy in iterated prisoner's dilemma', Proceedings of the International Conference on Modeling and Simulation (MS 2006), Kuala Lumpur, Malaysia (2006) [E1]
2006Chiong R, Beng OK, 'A comparison between genetic algorithms and evolutionary programming based on cutting stock problem', IMECS 2006: International Multiconference of Engineers and Computer Scientists, Kowloon, PEOPLES R CHINA (2006) [E1]
Author URL
2004Jankovic L, Chiong R, 'Investigation of strategy dynamics using prisoner's dilemma problem (invited paper)', Proceedings of the 5th International Conference on Computer Games: Artificial Intelligence, Design and Education (CGAIDE 2004), Wolverhampton, UK (2004) [E1]
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Grants and Funding

Summary

Number of grants5
Total funding$29,044

Click on a grant title below to expand the full details for that specific grant.


20143 grants / $14,044

A Framework for Next Generation Algorithm Benchmarking: Performance Testing and Community Benchmarking$10,044

Funding body: University of Newcastle

Funding bodyUniversity of Newcastle
Project Team
SchemeDiscovery Project
RoleLead
Funding Start2014
Funding Finish2014
GNo
Type Of FundingInternal
CategoryINTE
UONY

Faculty ECA Networking/Conference Grant 2014$2,000

Funding body: University of Newcastle - Faculty of Science & IT

Funding bodyUniversity of Newcastle - Faculty of Science & IT
Project TeamDoctor Raymond Chiong
SchemeEarly Career Academic (ECA) Networking/Conference Grant
RoleLead
Funding Start2014
Funding Finish2014
GNoG1401057
Type Of FundingInternal
CategoryINTE
UONY

Faculty PVC Conference Assistance Grant 2014$2,000

Funding body: University of Newcastle - Faculty of Science & IT

Funding bodyUniversity of Newcastle - Faculty of Science & IT
Project TeamDoctor Raymond Chiong
SchemePVC Conference Assistance Grant
RoleLead
Funding Start2014
Funding Finish2014
GNoG1401185
Type Of FundingInternal
CategoryINTE
UONY

20132 grants / $15,000

Intelligent Transportation Planning: Benchmarking of Novel Business Analytics Techniques using the Travelling Salesman Problem as a Test-Bed.$10,000

Funding body: University of Newcastle - Faculty of Science & IT

Funding bodyUniversity of Newcastle - Faculty of Science & IT
Project TeamDoctor Raymond Chiong, Associate Professor Claus Weise, Doctor Rukshan Athauda
SchemeStrategic Initiative Research Fund (SIRF)
RoleLead
Funding Start2013
Funding Finish2013
GNoG1401032
Type Of FundingInternal
CategoryINTE
UONY

“Stigmergy, mobility and the evolution of cooperation”. $5,000

Funding body: University of Newcastle

Funding bodyUniversity of Newcastle
Project TeamDoctor Raymond Chiong
SchemeNew Staff Grant
RoleLead
Funding Start2013
Funding Finish2013
GNoG1301045
Type Of FundingInternal
CategoryINTE
UONY
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Research Supervision

Current Supervision

CommencedResearch Title / Program / Supervisor Type
2014Identifying High-Value Social Entities from Twitter with Machine Learning and Multilingual Analysis.
Information Technology, Faculty of Science and Information Technology
Co-Supervisor
2014Identifying High-Value Social Entities from Twitter with Machine Learning and Multilingual Analysis.
Information Technology, Faculty of Science and Information Technology
Principal Supervisor
2013Towards Developing Computer Supported Collaborative Learning (CSCL) Scripts to Enhance Online Learning Experiences
Information Technology, Faculty of Science and Information Technology
Co-Supervisor
2011Memetic Algorithm-based Electricity Load Forecasting Models with Applications
Artificial Intelligence, Unknown
Co-Supervisor
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Research Collaborations

The map is a representation of a researchers co-authorship with collaborators across the globe. The map displays the number of publications against a country, where there is at least one co-author based in that country. Data is sourced from the University of Newcastle research publication management system (NURO) and may not fully represent the authors complete body of work.

CountryCount of Publications
Australia43
Malaysia33
China23
Germany5
United Kingdom5
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Dr Raymond Chiong

Position

Lecturer
School of Design Communication and IT
Faculty of Science and Information Technology

Focus area

Information Technology

Contact Details

Emailraymond.chiong@newcastle.edu.au
Phone(02) 4921 7367
Fax(02) 4921 5896

Office

RoomMCG15
BuildingMcMullin Building
LocationCallaghan
University Drive
Callaghan, NSW 2308
Australia
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