Abstract: EFootprints of the opposition concept can be observed in many areas around us. But it has sometimes been called by different names. Opposite particles in physics, complement of an event in probability, absolute or relative complement in set theory, and theses and antitheses in dialectic just are some examples to mention. Recently for the first time, Opposition-Based Learning (OBL) was proposed and then the opposition-based methods have been introduced in different artificial intelligence areas. All of them have tried to enhance searching or leaning process by utilizing the opposition concept. Opposition-based evolutionary algorithms, opposition-based neural networks, and also opposition-based reinforcement learning are some efforts in this direction. The main idea behind OBL is the simultaneous consideration of a candidate and its corresponding opposite candidate in order to achieve a better approximation for the current solution. The first and second parts of this lecture introduce the opposition-based sampling and its applications in various soft computing techniques and center-based sampling, respectively.
Population-based algorithms, such as Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Evolutionary Strategies (ES) are commonly used approaches to solve complex problems from science and engineering. They work with a population of candidate solutions. In this lecture, a novel center-based sampling is introduced for these algorithms. Reducing the number of function evaluations to tackle with high-dimensional problems is a worthwhile attempt; the proposed center-based sampling can open a new research area in this direction. Our simulation results confirm that this kind of sampling, which can be utilized during population initialization and/or generating successive generations, can be valuable in solving high-dimensional problems efficiently. Quasi-Oppositional Differential Evolution (QODE) will briefly be discussed as an evidence to support the proposed sampling theory. Finally, the opposition-based sampling and center-based sampling will be compared in this lecture.

Brief Biography of the Speaker:
Dr. Shahryar received his B.Sc. and M.Sc. degrees both with honors in software engineering from Shahid Beheshti University, Iran. He is holding a PhD degree in evolutionary computation from University of Waterloo, Canada. Opposition-based differential evolution (ODE) was proposed in his PhD thesis. He was a chief research manager at OMISA Inc. (Omni-Modality Intelligent Segmentation Assistant); a company which develops innovative software for medical image segmentation. Before joining to faculty of engineering and applied science at UOIT, Canada, as a tenure-track faculty member, he was a postdoctoral fellow at Simon Fraser University (SFU), Canada. His research includes evolutionary computation and image processing. Dr. Shahryar was awarded the Ontario Graduate Scholarship (OGS), President’s Graduate Scholarship (PGS), NSERC’s Japan Society for the Promotion of Science (JSPS) Fellowship, NSERC’s Industrial R&D Fellowship (IRDF), NSERC’s Visiting Fellowship in Canadian Government Laboratories (VF), and the Canadian Institute of Health Research (CIHR) Fellowship for two times. He was a CIHR research fellow for two years at Medial Imaging Department at Robarts Research Institute, Canada. During the PhD program, he published six journal papers, three book chapters, and 17 conference papers. Furthermore, one of his PhD works is a part of a patent which was registered by University of Waterloo.