Found Of Adaptive Optimization

Catalog description

This course is a survey of the newer, most common adaptive search methods. This is a project- and research-oriented course designed to give graduate students a foundation from which to explore areas of their own interest. This course is designed for students majoring in science and engineering. Focused topics include metaheuristic algorithms, such as simulated annealing, genetic algorithms, and particle swarm optimization. Topics, including Bayesian optimization, adaptive sampling, and other popular algorithms in the scope of adaptive optimization, will be partially covered. Issues such as solution encodings, stochastic convergence, selection methods, and local and global search methods are discussed. Students are encouraged to apply the approaches they have been taught to their research topics and present their work and findings during the lecture, final presentation, and report. Prerequisite: SSIE 505 or equivalent, SSIE 520, and knowledge of at least one programming language. Offered in the Fall semester.