Title: Augmenting Human Intelligence (AHI) for Practical Decision Making

Ruhul A Sarker obtained his PhD from Dalhousie University (former TUNS), Halifax, Canada in 1992, and B.Sc.Eng. (1982) and M.Eng. (1984) from Bangladesh University of Engineering and Technology (BUET), Dhaka. He is currently a senior Professor in the School of Engineering and IT (SEIT) at the University of New South Wales, Canberra campus (UNSW Canberra). He served as the Director of PG Research at UNSW Canberra (2015 to 2020) and also as the Deputy Head of School (Research) of SEIT (2011-2014). Prof. Sarker’s broad research interests are decision analytics, CI / evolutionary computation, and applied optimization with an emphasis on Augmenting Human Intelligence (AHI). His name appeared on the lists of top 2% of world's scientists-researchers published by Stanford University and also Elsevier/Scopus/digitalcommonsdata (Research fields: Artificial Intelligence and Operations Research). Prof. Sarker has published 410+ papers in the top journals and conferences in his areas of research. As of GoogleScholar, his papers were cited more than 11,500 times and his current h-index is 55. He has received numerous awards from international conferences, algorithm competitions and professional bodies. He has been invited to deliver keynote speech in many international conferences including IEEE WCCI 2020 and IEEE SSCI 2022. Prof. Sarker has successfully obtained more than A$4 million external research grants mainly from Australian Research Council (ARC) and Australian Defence Science and Technology Group (DSTG). He is the lead author of the book Optimization Modelling: A Practical Approach, Taylor & Francis /CRC Press, Boca Raton. Currently, he is an associate editor of three international journals. 

Abstract: Many practical decisions are made through complex problem-solving. Over the last few decades, Computational Intelligence (CI) has become popular for solving complex decision problems. Computational intelligence techniques augment human problem-solving logic (/intelligence) using computational power. Although complex problem-solving is not a new field of research, the configuration of CI-based algorithms for solving complex problems is an emerging topic for research.  In this talk, we will discuss some specific CI-based approaches, particularly evolutionary computation, and their contributions to real-world decision-making
processes. We will provide examples of solution representation, initialisation, search operators, algorithmic parameters, and algorithm framework for algorithm configuration. Our experiences with a number of practical issues both in real-world decision problems, and their solution approaches will be shared.