Seminar 1:
Title: Artificial Intelligence:
Methods and Applications
Abstract: This seminar helps to
understand problem solving capability of soft computing methods, including
fuzzy expert systems, evolutionary computation, and artificial neural networks.
In this direction, the presented topics cover weaknesses, limitations, and
strengths of the variant well-known AI algorithms to tackle with real-world
problems. The problems would be analyzed in detail to choose or design an
intelligent method or a hybrid intelligent system to solve them properly. Many
practical examples are utilized to make understanding of the topics easier.
This tutorial would be very useful for graduate students, researchers, and
faculty members in all engineering and science majors.
Date and Location:: Wednesday
19th February 2014 @ 110:00 AM in Room 105 - Building 22.
Seminar
2
Title: Opposition-Based Soft
Computing
Abstract: Footprints of the opposition concept can be
observed in many areas around us. But it has sometimes been known by different
names. Opposite particles in physics, complement of an event in probability,
and absolute or relative complement in set theory just are some examples to
mention. But for the first time, recently, Opposition-Based Learning (OBL) was
proposed and then the opposition-based approaches have been introduced in
variant soft computing areas. All of them have tried to enhance optimization or
leaning process by utilizing an opposition scheme. Opposition-based
evolutionary algorithms, opposition-based neural networks, and also
opposition-based reinforcement learning are some efforts in this direction.
Since 2005, more than 200 papers have been published about the opposition-based
algorithms; which have been received more than 1800 citations so far. This talk
will introduce Opposition-Based Computation (OBC) in general and also its
possible variant applications in enhancing soft computing techniques, such as
evolutionary computation, fuzzy systems, reinforcement learning, and artificial
neural networks. The presentation would be fully interactive to cover all
related experiments, mathematical proofs, and intuitive explanations of the
opposition-based schemes.
Date and Location: Wednesday
19th February 2014 @ 02:10 PM in Room 132 - Building 22.
Short Bio: Dr. Shahryar
Rahnamayan is an Associate Professor at the University of Ontario, Canada. His area of
specialization includes:
·
Machine
Learning
·
Artificial
Intelligence
·
Solving
Large-Scale Problems
· Multi-Objective,
Non-Convex, Nonlinear, and Complex Optimization with its real-world
applications
· Parallel
Processing