Stochastic Optimization Algorithms

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 The course begins with a brief survey of classical optimization methods, such as Newton's method, Lagrange multiplier methods, gradient descent etc. Next, three stochastic optimization methods are considered, namely evolutionary algorithms (inspired by darwinian evolution), and ant colony optimization (inspired by the group behavior of ants), and particle swarm optimization (inspired by swarming, as exhibited by, for example, birds and fish). In addition to learning the theoretical background of these algorithms, the students also apply the algorithms in problems of varying degrees of complexity.

Student portal page: http://www.student.chalmers.se/sp/course?course_id=14383

External course webpage: http://www.me.chalmers.se/~mwahde/courses/soa/2016/FFR105.html

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Mattias Wahde

Professor Mattias Wahde is researcher and teacher within the research group Adaptive Systems.The ultimate aim of his research is to generate autonomous robots capable of carrying out a variety of relevant tasks, particularly dangerous or tedious tasks which are presently carried out by people. His research is focused on generating robotic brains (control systems) rather than hardware (robots). In particular, he is developing a method (the utility function method) for behavioral selection. This method, as well as his research in general, is based on biologically inspired computation methods, particularly evolutionary algorithms (EAs).

Position: Professor

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