Keynote Speaker: Prof. Dr. Mahmoud Abdel-Aty
Google Scholar: https://scholar.google.com/citations?user=1m5qGCQAAAAJ&hl=en
Prof. Mahmoud Abdel-Aty completed his doctorate in quantum optics at Max-Planck-Institute for Quantum Optics, Munch, Germany in 1999. He received the D. Sc. (Doctor of Science), in 2007.
His significant contributions in quantum algorithms, measurement theories, nanomechanical modeling, and the development of the widely known Yang-Abdel-Aty-Carlo fractional derivative operator in general fractional calculus have earned him recognition. He has also been awarded two patents for his innovative work in developing new quantum algorithms.
Abdel-Aty’s research has been widely recognized and he has received several local and international awards, such as the Incentive State Award for Advanced Technology Sciences, the State Award of Excellence for Basic Science and State Encouragement Award for Mathematics. He was also the recipient of the Abdul Hameed Shoman Foundation Award for Arab Researchers in Mathematics and Computer Sciences and Mohamed bin Rashed Prize for the best initiative in language policy and planning offered by Mohamed bin Rashed Foundation. He was also the recipient of the Third World Academy of Sciences, Fayza Al-Khorafy award and Shawky Salem prize for Knowledge Management. In 2014 he has been elected as a vice-president of the African Academy of Science, 2018 elected as a President of National Committee for mathematics, 2022 elected as a Vice-President of African mathematical Union, 2023 elected as a Vice-President of Egyptian mathematical Society.
Lecture Title : Quantum Computing and Artificial Intelligence
Keynote Speaker: Dr. Ahmed Solyman
received the B.Eng. and the M.S. degree in electrical and electronics engineering from MTC, Egypt, in 1999 and 2006, respectively, and a Ph.D. degree in electrical and electronics engineering from the University of Strathclyde, U.K., in 2013. He joined the Egyptian research center in 1999; he contributed to the design and analysis of many communication and encryption devices. In 2018, he joined the Electrical and Electric Engineering department at Modern University for Technology and Information, Cairo, Egypt, as an assistant professor. From 2019 to 2022, he is a postdoctoral Fellowship for Research Abroad as visiting professor at the Istanbul Gelisim University. Currently, he is an Assistant Professor at the Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Nişantaşı University. His research interests include wireless communication networks, digital signal processing, IoT, bioinformatics, and artificial intelligence. He has co-authored one book and has published about 50 refereed professional research papers. He has completed 10 MSEE thesis students.
Keynote Speaker: Dr Mehmet Emin Aydin
Dr Mehmet Emin Aydin is Senior Lecturer in Computer Science at the University of the West of England. He has received BSc, MA and PhD degrees from Istanbul Technical University, Istanbul University and Sakarya University, respectively. His research interests include machine learning, multi agent systems, multi-agent reinforcement learning , metaheuristics, swarm intelligence, resource planning, scheduling and optimization. He has secured several research grants individually and in collaboration, published one edited research book, 120+ journal and conference articles, conducted several guest editorial of special issues and successfully supervised many PhD and post-doctoral supervisions. In addition to being the member of advisory committees of many international conferences, he is editorial board member of various peer-reviewed international journals. He is currently fellow of Higher Education Academy, member of EPSRC College in the UK, and senior member of ACM and IEEE.
An explorative journey towards generalisation in problem solving
Problem solving is one of renown artificial intelligence fields, which has kept attracting research for decades. In the past two decades, metaheuristic optimisation and swarm intelligence algorithms (a.k.a. heuristic search algorithms) have been increasingly popular, particularly in logistic, science, and engineering problems and are recognised as the family of the state-of-art approaches in problem solving. The main challenge appears to be in the speed of algorithmic approximation where many approaches were proposed to accelerate and not to spin in local optima. The balance between explorative and exploitive operations appear to play a crucial role in this process.
The fundamental characteristics of heuristic search algorithms are that they are dependent on parametric structures and search strategies. Some online and offline strategies are employed to obtain optimal configurations for the algorithms. Adaptive operator selection is one of strategies that heuristic algorithms use, which plays a crucial role in the efficiency of heuristic-based problem-solving algorithms, especially, when a pool of operators is used to let algorithms dynamically select operators to produce new candidate solutions. A sequence of selected operators is built up throughout the search which impacts the success of the algorithms. Successive operators in a bespoke sequence can be complementary and therefore diversify the search while randomly selected operators are not expected to behave in this way. However, recent research demonstrates that the state-of-art adaptive selection schemes have been proposed to select the best next operator without considering the problem state in the process. In addition, the inefficiencies in problem solving with search procedures can be avoided using the experiences gained while search is undergoing utilising machine learning approaches. In the field of machine learning, reinforcement learning refers to goal-oriented algorithms, which learn from the environment how to achieve a goal.
Recently, we conducted research on that if we can build up more generic approach which can retain gained experiences and exploit for avoidance of inefficiencies in the search process. Binary problem solving is one of the approaches alongside use of adaptively selected multiple operators. Secondly, reinforcement learning was proposed to be embedded in search algorithms for taking the problem state on board in operator selection process. The proposed approach implies mapping the problem states to the best fitting operators in the pool so as to achieve higher diversity and shape up an optimum operator sequence throughout the search process. However, despite significant performance improvement, it is observed that learned information may be transferred from one problem-solving procedure to another. The third lag of our studies was to investigate how to retain the gained experiences for longer use and for transferrable use across problems with various size and types. The research results in these regard will be summarised over solving few types of combinatorial optimisation problems.