31 January-2 February 2017, Melbourne, Australia
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Invited Speakers

Hussein Abbass, University of New South Wales
Hussein Abbass is a Professor of Information Technology at the University of New South Wales in Canberra (UNSW-Canberra), Australia. He is a fellow of the Australian Computer Society (FACS), a fellow of the Operational Research Society (FORS,UK); a fellow of the Australian Institute of Management (FAIM), the President of the Australian Society for Operations Research, and the Vice-president for Technical Activities (2016-2017) for the IEEE Computational Intelligence Society. He is an associate Editor of the IEEE Trans. On Evolutionary Computation, IEEE Trans. on Cybernetics, IEEE Trans. on Cognitive and Developmental Systems, IEEE Computational Intelligence Magazine, and four other journals. His current research contributes to trusted autonomy with an aim to design next generation trusted artificial intelligence systems that seamlessly integrate humans and machines. His work fuses artificial intelligence, big data, cognitive science, operations research, and robotics.
Topic: Trusted Autonomy: Challenges and Opportunities for Computational Intelligence
Trusted Autonomy (TA) is the wider scientific endeavour to establish the groundwork and basic research in science and engineering required to develop Trusted Autonomous Systems [2]. Autonomous Systems are leaving the laboratory environment. Will they be used? Will they be safe? Are they ready for a harsh unpredictable environment? Are they ready to interact with humans? Can they understand humans? Can they feel humans? Can they trust humans? Will they be trusted?

More questions exist today on autonomous systems than ever before. As the technology becomes technologically mature, answers to questions on social maturity will become the decisive factor on whether or not these technologies will be allowed on the streets, in hospitals, schools, or even in the battlespace.

This talk will present on the challenges facing TA, raise questions more than answers, and offer suggestions for researchers in the field of Computational Intelligence (CI) to work on some of the key challenges in TA; explaining why it is the right time for CI techniques to showcase their utility in this fast evolving field of research.

Related publications:
[1] Abbass H.A., Petraki E., Merrick K., Harvey J., and Barlow M. (2016a) Trusted Autonomy and Cognitive Cyber Symbiosis: Open Challenges, Cognitive Computation, 8(3), 385-408. 10. 1007/s12559-015-9365-5. [ open access ]
[2] Abbass H.A., Leu G., and Merrick K. (2016b) A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data, IEEE Access, 4, 2808 - 2830, doi:10.1109/ACCESS.2016.2571058. [ open access ]

Zbigniew Michalewicz, University of Adelaide & Complexica Pty Ltd
Zbigniew Michalewicz is the Chief Scientist of Complexica, an Artificial Intelligence software company that helps large organisations sell more products and services, at a higher margin, through the use of automated analytics. He is also Emeritus Professor at the School of Computer Science, University of Adelaide and holds Professor positions at the Institute of Computer Science, Polish Academy of Sciences, at the Polish-Japanese Academy of Information Technology, and an honorary Professor position at State Key Laboratory of Software Engineering of Wuhan University, China. He is also associated with Structural Complexity Laboratory at Seoul National University, South Korea. In December 2013 he was awarded (by the President of Poland, Mr. Bronislaw Komorowski) the Order of the Rebirth of Polish Polonia Restituta - the second highest Polish state decoration civilian (after the Order of the White Eagle), awarded for outstanding achievements in the field of education, science, sports, culture, arts, economy, national defence, social activities, the civil service and the development of good relations with other countries.

For many years his research interests were in the field of evolutionary computation. He published several books, including a monograph Genetic Algorithms + Data Structures = Evolution Programs (3 editions, a few translations, over 18,300 citations, source: Google Scholar), and over 250 technical papers in journals and conference proceedings that are cited widely (over 40,000 citations, source: Google Scholar). He was one of the editors-in-chief of the Handbook of Evolutionary Computation and the general chairman of the First IEEE International Conference on Evolutionary Computation held in Orlando, June 1994.

Zbigniew Michalewicz has over 35 years of academic and industry experiences, and possesses expert knowledge of numerous Artificial Intelligence technologies. He was the co-Founder and Chief Scientist of NuTech Solutions, which was acquired by Netezza and subsequently by IBM, and the co-Founder and Chief Scientist of SolveIT Software, which was acquired by Schneider Electric after becoming the 3rd fastest growing company in Australia. Both companies grew to approximately 200 employees before they were being acquired.

During his time in the corporate world, Professor Michalewicz led numerous large-scale predictive analytics and optimisation projects for major corporations, including Ford Motor Company, BHP Billiton, U.S. Department of Defence, and Bank of America. Professor Michalewicz also served as the Chairman of the Technical Committee on Evolutionary Computation, and later as the Executive Vice President of IEEE Neural Network Council.
Topic: Increasing Sales through Automated Analytics
The talk is on business applications for transforming data into decisions, based on work done for 3 companies (NuTech Solutions, SolveIT Software, and Complexica) over the last 16 years. A few general concepts would be discussed, illustrated this by a few examples - from NuTech, from SolveIT, and from Complexica. The final set of examples would illustrate Complexica’s approach for increasing sales (revenue, margin, and customer engagement) through automated analysis.

Pablo Moscato, The University of Newcastle
Prof. Pablo Moscato is an Australian Research Council Future Fellow and Professor of Computer Science at The University of Newcastle. At the California Institute of Technology (1988-89) he developed a methodology called "memetic algorithms" which is now widely used around the world in Artificial Intelligence, Data Science, and Business and Consumer Analytics.

Prof. Moscato is Founding Director of the Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-based Medicine (2007-2015) and the Funding Director of Newcastle Bioinformatics Initiative (2002-2006) of The University of Newcastle. His expertise in Data Science was then essential for a large number of applied projects.

He has been working in Applied Mathematics for 30 years, and in heuristic methods for Operations Research problems since 1985. His work and ideas have been highly influential in a large number of scientific and technological fields and his manuscripts have been highly cited. The journal "Memetic Computing" is largely dedicated to a methodology he championed (memetic algorithms). Every 48 hours a new published paper brings a novel application of these techniques. Due this work and his other contributions in the areas of classification and machine learning, Prof. Moscato is now well-respected around the world and he has become one of Australia's most cited computer scientists.
Topic: “We have it all wrong”… so what are you doing to change practice?
Along with many other researchers, I share the view that a systematically coherent research program, in both theory and applications of algorithms, is definitely needed to accelerate innovation in computing. We routinely design computational approaches and engage in healthy competitions where the performance of our methods is tested… but what if “We have it all wrong”? What if we need a paradigmatic change in our practice for the development and design of computational methods? We may need to enrich our practice with a new approach.

In fact, John N. Hooker already alerted the computing and mathematical community more than 20 years ago [Hooker, 1995; Journal of Heuristics]: “Competitive testing tells us which algorithm is faster but not why.” Hooker argued for a more scientific approach and he proposed the use of ‘controlled experimentation’. This is common in empirical sciences. “Based on one’s insights into an algorithm”, he said, “one may expect good performance to depend on a certain problem characteristic”. Then “design a controlled experiment that checks how the presence or absence of this characteristic affects performance” and, finally, “build an exploratory mathematical model that captures the insight […] and deduce from its precise consequences that can be put to the test”.

In this talk, I will address how a new thinking is needed for the development of our field. I will have an with emphasis in our success on both speeding up solutions for the traveling salesman problem as well as our success to create very hard instances for the world’s fastest solver.

Kate Smith-Miles, Monash University
Kate Smith-Miles is a Professor in the School of Mathematical Sciences at Monash University in Australia, where she was Head of School from 2009-2014. She currently holds a Laureate Fellowship from the Australian Research Council (2014-2019) to conduct research into new methodologies to gain insights into algorithm strengths and weaknesses. She is also the inaugural Director of MAXIMA (the Monash Academy for Cross & Interdisciplinary Mathematical Applications). Kate obtained a B.Sc.(Hons) in Mathematics and a Ph.D. in Electrical Engineering, both from the University of Melbourne, Australia. She has published 2 books on neural networks and data mining applications, and over 240 refereed journal and international conference papers in the areas of neural networks, combinatorial optimization, intelligent systems and data mining. She has supervised to completion 23 PhD students, and has been awarded over AUD$15 million in competitive grants, including 13 Australian Research Council grants and industry awards. From December 2016 she will be President of the Australian Mathematical Society. She was elected Fellow of the Institute of Engineers Australia (FIEAust) in 2006, and Fellow of the Australian Mathematical Society (FAustMS) in 2008. She was awarded the Australian Mathematical Society Medal in 2010 for distinguished research. In addition to her academic activities, she also regularly acts as a consultant to industry in the areas of optimisation, data mining, and intelligent systems.
Topic: Instance Spaces for Insightful Performance Evaluation
Objective assessment of algorithm performance is notoriously difficult, with conclusions often inadvertently biased towards the chosen test instances. Rather than reporting average performance of algorithms across a set of chosen instances, we discuss a new methodology to enable the strengths and weaknesses of different algorithms to be compared across a broader generalised instance space. Initially developed for combinatorial optimisation, the methodology has recently been extended the domains of continuous optimisation and machine learning classification. Results will be presented across these problem domains to demonstrate: (i) how pockets of the instance space can be found where algorithm performance varies significantly from the average performance of an algorithm; (ii) how the properties of the instances can be used to predict algorithm performance on previously unseen instances with high accuracy; (iii) how the relative strengths and weaknesses of each algorithm can be visualized and measured objectively; and (iv) how new test instances can be generated to fill the instance space and provide insights into algorithmic power.

Mengjie Zhang, Victoria University of Wellington
Mengjie Zhang is currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group with 10 staff members and over 20 PhD students. He is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, a member of the Faculty of Graduate Research Board at the University, Associate Dean (Research and Innovation) for Faculty of Engineering, and Chair of the Research Committee for the School of Engineering and Computer Science. His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimisation and learning classifier systems with application areas of computer vision and image processing, multi-objective optimisation, and feature selection and dimension reduction for classification with high dimensions, transfer learning, classification with missing data, and scheduling and combinatorial optimisation. Prof Zhang has published over 400 research papers in fully refereed international journals and conferences in these areas. He has been supervising over 100 research thesis and project students including over 30 PhD students.

He has been serving as an associated editor or editorial board member for eight international journals including IEEE Transactions on Evolutionary Computation, the Evolutionary Computation Journal (MIT Press), Genetic Programming and Evolvable Machines (Springer), Applied Soft Computing, Natural Computing, and Engineering Applications of Artificial Intelligence, and as a reviewer of over 30 international journals. He has been a major chair for over ten international conferences including IEEE CEC, GECCO, EvoStar and SEAL. He has also been serving as a steering committee member and a program committee member for over 80 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed as one of the top ten world genetic programming researchers by the GP bibliography.

Prof Zhang is a senior member of IEEE and a member of ACM. He is currently chairing the IEEE CIS Emergent Technologies Technical Committee consisting of over 40 top CI researchers from the five continents and 17 task forces. He is the immediate Past Chair for the IEEE CIS Evolutionary Computation Technical Committee and a member of the IEEE CIS Award Committee. He is also a member of IEEE CIS Intelligent System Applications Technical Committee, a vice-chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, a vice-chair of the Task Force on Evolutionary Computer Vision and Image Processing, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.
Topic: Recent Development of Genetic Programming and Applications
One of the central challenges of computer science is to use a computer to do what needs to be done without telling it/knowing the specific process. Genetic programming (GP) addresses this challenge by providing a method for automatically creating a working computer program from a high-level statement of a specific task. GP achieves this goal by genetically breeding a population of computer programs using the principles of Darwinian natural selection and biologically inspired operations. This talk will start with an overview of GP principles, including representation, operators, search mechanisms and the evolutionary process. The talk will then discuss the most popular applications of GP with a focus on the evolved "models" and "generalisation" on symbolic regression and mathematical modelling, classification with unbalanced data, feature selection and construction, and dynamic job shop scheduling. The talk will end with some interesting demonstrations and "deep [learning] program structures" in image recognition.

David G. Green, Monash University
David Green is Professor of Information Technology at Monash University. He is one of Australia’s leading experts on complexity theory. His proof of the universality of networks showed that networks are inherent in the structure and behaviour of all complex systems. More recently, he pioneered the theory of dual phase evolution, a process that explains the way networks contribute to the emergence of order in many natural and artificial systems. In the course his research on complexity and evolutionary computing he has investigated problems as diverse as forest ecology, proteins, geographic information and social networks, and natural computation. He is the author of 9 books, including Of Ants and Men (2014), Dual-Phase Evolution (2014), Complexity in Landscape Ecology (2006), and The Serendipity Machine (2004). He is also the author of more than 200 research articles on complexity theory, evolutionary computing, and multi-agent systems. For further information, please visit his online profile.
Topic: The Network Theory of Complexity
Modern society deals increasingly with complex networks. Networks are graphs (nodes linked by arcs or edges) in which the nodes and/or edges have attributes (e.g., names or sizes). The network theory of complexity treats properties and processes of complex systems that arise from their underlying networks. The theory makes strong claims about the universality of networks as well as its relationship to other theories, such as computational complexity, optimization and Kolmogoroff-Chaitin complexity.

Networks, and network based methods, have gained increasing prominence in many areas of research. This talk explains the fundamentals of network theory, surveys its applications in various areas of research and outlines some insights from recent research. It will emphasize important tools, resources and methods available to researchers.

Theoretical topics covered will include the connectivity avalanche and its implications, network topologies, motifs, modules, and network metrics. It will also explain several processes in networks that are known to promote emergent properties, especially feedback, encapsulation and dual-phase evolution.

The talk will include a brief survey of implications of the network properties listed above for understanding real-world networks. Examples include biological (food webs, genetic regulatory networks), social networks (group decision-making), technical (computation and communication networks), and socio-technical (accidents, system failure and supply networks). The talk will conclude by touching on recent developments, such as interdependent networks.

Important Dates
Final submission deadline (no further extension): 10 September 2016
Decision notification: 17 October 2016
Camera ready submission: 7 November 2016
Conference dates: 31 January-2 February 2017

Copyright ACALCI 2017