Prof. Hisao Ishibuchi

Professor, Graduate School of Engineering, Osaka Prefecture University, Japan

Title: Evolutionary Many-Objective Optimization

Abstract. Many-objective optimization has been a hot topic in the EMO (evolutionary multiobjective optimization) research field for a decade. This is because many-objective problems are difficult for EMO algorithms. Frequently-used EMO algorithms such as NSGA-II and SPEA2 do not work well on many-objective problems. A number of approaches have been proposed for improving the search ability of existing algorithms. New algorithms have also been proposed for the handling of many-objective problems. This talk starts with a brief introduction to the EMO research field through a short review on co-evolutionary development of EMO algorithms and test problems in the last three decades. Next, difficulties of many-objective optimization are explained together with performance comparison results of some representative EMO algorithms (NSGA-II, MOEA/D, SMS-EMOA and HypE) on multi-objective and many-objective problems. Then, current trends in algorithm development for many-objective problems are briefly explained. Finally, difficulties in performance evaluation of many-objective algorithms are explained as future research topics.

Biography. Hisao Ishibuchi received the BS and MS degrees from Kyoto University in 1985 and 1987, respectively. In 1992, he received the Ph. D. degree from Osaka Prefecture University where he has been a full professor since 1999. He received a Best Paper Award from GECCO 2004, HIS-NCEI 2006, FUZZ-IEEE 2009, WAC 2010, SCIS & ISIS 2010, FUZZ-IEEE 2011 and ACIIDS 2015. He also received a 2007 JSPS Prize. He was the IEEE CIS Vice-President for Technical Activities (2010-2013), the General Chair of ICMLA 2011, the Program Chair of CEC 2010 and IES 2014, and a Program/Technical Co-Chair of Fuzzy IEEE 2006, 2011-2013, 2015 and CEC 2013-2014. Currently, he is the Editor-in-Chief of IEEE CI Magazine (2014-2015), an IEEE CIS AdCom member (2014-2016), an IEEE CIS Distinguished Lecturer (2015-2017), and an Associate Editor of IEEE TEVC (2007-2015), IEEE Access (2013-2015) and IEEE TCyb (2013-2015). He is an IEEE Fellow. According to Google Scholar, the total number of citations of his publications is more than 16,000 and his h-index is 56 (June 1, 2015).

Prof. Yew Soon Ong

Associate Professor and Director, Center for Computational Intelligence, Nanyang Technological University, Singapore

Director, A*Star SIMTECH-NTU Joint Lab on Complex Systems, School of Computer Engineering, Nanyang Technological University, Singapore

Programme Principal Investigator, Rolls-Royce@NTU Corporate Lab

Title: Towards Evolutionary Multitasking: A New Paradigm in Evolutionary Computation

Abstract. The design of population-based search algorithms of evolutionary computation (EC) has traditionally been focused on efficiently solving a single optimization task at a time. It is only very recently that a new paradigm in EC, namely, multifactorial optimization (MFO), has been introduced to explore the potential of evolutionary multitasking (Gupta, Ong, & Feng, 2015). The nomenclature signifies a multitasking search involving multiple optimization tasks at once, with each task contributing a unique factor influencing the evolution of a single population of individuals. MFO is found to leverage the scope for implicit genetic transfer offered by the population 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 unrelated tasks. A strong practical motivation for the paradigm is derived from the rapidly expanding popularity of cloud computing (CC) services. It is noted that CC characteristically provides an environment in which multiple jobs can be received from multiple users at the same time. Thus, assuming each job to correspond to some kind of optimization task, as may be the case in a cloud-based on-demand optimization service, the CC environment is expected to lend itself nicely to the unique features of MFO.

In this talk, the formalization of the concept of MFO is first introduced. A fitness landscape-based approach towards understanding what is truly meant by there being underlying synergies (or what we term as genetic complementarities) between optimization tasks is then discussed. Accordingly, a synergy metric capable of quantifying the complementarity, which shall later be shown to act as a “qualitative” predictor of the success of multitasking is also presented (Gupta, Ong, Da, Feng, and Handoko, 2015). With the above in mind, a novel evolutionary algorithm (EA) for MFO is proposed, one that is inspired by bio-cultural models of multifactorial inheritance, so as to best harness the genetic complementarity between tasks. The salient feature of the algorithm is that it incorporates a unified solution representation scheme which, to a large extent, unites the fields of continuous and discrete optimization. The efficacy of the proposed algorithm, and the concept of MFO in general, shall finally be substantiated via a variety of computation experiments in intra and inter-domain evolutionary multitasking.

Biography. Yew-Soon Ong is currently an Associate Professor and Director of the A*Star SIMTECH-NTU Joint Lab on Complex Systems at the School of Computer Engineering, Nanyang Technological University, Singapore and Programme Principal Investigator of the Rolls-Royce@NTU Corporate Lab on Data Analytics and Complex Systems. He received a PhD degree on Artificial Intelligence in complex design from the Computational Engineering and Design Center, University of Southampton, United Kingdom in 2003. His current research interest in computational intelligence spans across memetic & evolutionary computation, machine learning, Big Data Analytics, and intelligent multi-agents. He is the founding Technical Editor-in-Chief of the Memetic Computing Journal, founding Chief Editor of the Springer book series on studies in adaptation, learning, and optimization, Associate Editor 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 coauthored over 120 refereed publications and his research work on Memetic Algorithm was featured by Thomson Scientific's Essential Science Indicators as one of the most cited emerging area of research in August 2007. Recently, he 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. Presently, he is Conference Chair of the Congress on Evolutionary Computation, World Congress on Computational Intelligence, Vancouver, Canada, 2016 and is secretary of the IEEE Transactions on Computational Intelligence and AI in Games steering committee.

Prof. Sanghamitra Bandyopadhyay

Head, Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

Recipient of the prestigious Shanti Swarup Bhatnagar Prize in Engineering Sciences, 2010

Title: Advances in Computational Biology

Biography. Dr. Sanghamitra Bandyopadhyay did her B Tech, M Tech and Ph. D. in Computer Science from Calcutta University, IIT Kharagpur and ISI respectively. She is currently a Professor (Higher Administrative Grade) at the Indian Statistical Institute, Kolkata, India. She has worked in various Universities and Institutes world-wide including in USA, Australia, Germany, China, Italy and Mexico. She has delivered invited lectures in many more countries. She has authored/co-authored more than 145 journal papers and 140 articles in international conferences and book chapters, and published six authored and edited books from publishers like Springer, World Scientific and Wiley. She has also edited journals special issues in the area of soft computing, data mining, and bioinformatics. Her research interests include computational biology and bioinformatics, soft and evolutionary computation, pattern recognition and data mining. She is a Fellow of the National Academy of Sciences, Allahabad, India (NASI) and Indian National Academy of Engineering (INAE) and West Bengal Academy of Science and Technology, and senior member of the IEEE. Sanghamitra is the recipient of several prestigious awards including the Dr. Shanker Dayal Sharma Gold Medal and also the Institute Silver Medal from IIT, Kharagpur, India, the Young Scientist Awards of the Indian National Science Academy (INSA), the Indian Science Congress Association (ISCA), the Young Engineer Award of the Indian National Academy of Engineering (INAE), the Swarnajayanti fellowship from the Department of Science and Technology (DST), and the Humboldt Fellowship from Germany. She has been selected as a Senior Associate of ICTP, Italy. She was awarded the prestigious Shanti Swarup Bhatnagar Prize in Engineering Science.

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