Nanyang Technological University
Towards Evolutionary Multitasking: A New Paradigm
We are in an era where a plethora of computational problem-solving methodologies are being invented to tackle the diverse problems that are of interest to researchers. Some of these problems have emerged from real-life scenarios while some are theoretically motivated and created to stretch the bounds of current computational algorithms. Regardless, it is clear that in this new millennium a unifying concept to dissolve the barriers among these techniques will help to advance the course of algorithmic research. Interestingly, there is a parallel that can be drawn in memes from both socio-cultural and computational perspectives. The platform for memes in the former is the human minds while in the latter, the platform for memes is algorithms for problem-solving. In this context, memes can culminate into representations that enhance the problem-solving capability of algorithms. The phrase Memetic Computing has surfaced in recent years; emerging as a discipline of research that focuses on the use of memes as units of information which is analogous to memes in a social and cultural context. Memetic computing offers a broad scope, perpetuating the idea of memes into concepts that capture the richness of algorithms that defines a new generation of computational methodologies. It is defined as a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of problem solving.
In this talk, we take a peek into some state-of-the-art memetic algorithms and frameworks of memetic computation. In particular, the new paradigm of multitasking optimization, which was recently proposed and published online in the IEEE Transactions on Evolutionary Computation journal in 2015, is introduced. It was noted that traditional methods for optimization, including the population-based search algorithms of Evolutionary Computation (EC), have generally been focused on efficiently solving only a single optimization task at a time. It is only very recently that Multifactorial Optimization (MFO) has been developed to explore the potential for evolutionary multitasking. MFO is found to leverage the scope for implicit genetic transfer across problems in a simple and elegant manner, thereby, opening doors to a plethora of new research opportunities in EC, dealing, in particular, with the exploitation of underlying synergies between seemingly distinct tasks. Last but not least, some applications of evolutionary multitasking in Software Engineering is showcased.
Yew-Soon Ong is currently a Professor of Computer Science in the School of Computer Engineering at Nanyang Technological University (NTU), Singapore. He served as Director of the Computational Intelligence Research Centre from 2008 – 2015 and currently a Director of the A*Star SIMTECH-NTU Joint Lab on Complex Systems. He is also a Principal Investigator of the Data Analytics & Complex Systems Programme in the NTU-Rolls Royce Corporate Laboratory. He received his Bachelors and Master’s degrees in NTU and obtained his PhD degree on Artificial Intelligence in complex design from the Computational Engineering and Design Centre at the University of Southampton, United Kingdom.
His research focus is in computational intelligence (CI), particularly on evolutionary, memetic computation and machine learning. He is known for his research and development of new concepts and novel solutions and applications in memetic computation which mimics biological evolution and cultural evolution (or learning). He founded the Task Force on Memetic Computing under the IEEE Computational Intelligence Society Emergent Technology Technical Committee and served as its Chair from 2007 to 2010. In 2009, he also co-founded the Memetic Computing Journal and has been serving as its Technical Editor-in-Chief since. His works on memetic computation has been well received and he has delivered many talks as keynote, plenary or invited speaker at international conferences, workshops and research institutions worldwide. He was featured for his research work in memetic computation by the Thomson Scientific’s Essential Science Indicators as one of the most cited new area of research in August 2007. He also received the 2015 IEEE Computational Intelligence Magazine Outstanding Paper Award and the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award for his work pertaining to Memetic Computing. He is also founding chief editor of the Springer Book Series on Studies in Adaptation, Learning, and Optimization, and Associate Editor of many journals including the IEEE Transactions on Evolutionary Computation, the IEEE Transactions on Neural Networks & Learning Systems, IEEE Computational Intelligence Magazine, IEEE Transactions on Cybernetics, IEEE Transactions on Big Data, and others. He has filed several patents and innovative achievements in the area of computational intelligence. His research results have generated considerable commercialisation impacts and led to new start-ups. Dark-Dots, which is a CI enabled iOS game top the charts in 48 countries including USA, China and Singapore, was downloaded by well over 448,000 players worldwide when it was launched, with 27% of its players from China and 17% from the USA.
At the IEEE Computational Intelligence Society, he chaired the Intelligent Systems Applications Technical Committee from 2013-2014, the Emergent Technology Technical Committee from 2011-2012. He also participates actively in the organization of international conferences. He has served as General co-Chair of the 18th Asia Pacific Symposium of Intelligent and Evolutionary Systems (2014), Program co-Chair of the 9th International Conference on Simulated Evolution and Learning (2012), General co-Chair of the International Conference on Systems-Biology and Bioinformatics (2010 & 2011), and as local advisor of the Asian Conference on Machine Learning (2012). He has also served as Track Chair of the ACM Genetic and Evolutionary Computation Conference from 2011- 2014. Presently, he is Conference Chair of the 2016 IEEE Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Vancouver, Canada, and serves as secretary of the IEEE Transactions on Computational Intelligence and AI in Games steering committee.