The room is AA3195. I hope to see many of you there.
Title:
Evolutionary Computation and Machine Learning for the Engineering of Intelligent Systems
Abstract:
Evolutionary computation consists in black-box population-based
optimization techniques inspired by natural evolution. These
metaheuristics are quite successful at solving problems hard to tackle
with conventional methods, for example when dealing with strongly non
convex multimodal functions, multiple objectives, or solutions
represented a complex data structures (e.g., graphs, trees, symbolic
expressions). Although evolutionary computation is often considered as a
close sibling to machine learning, it is often badly understood and
dismissed by researchers in the field, mostly given its ad hoc nature
and the impression it lacks strong theoretical foundations. Such
considerations are unfortunate, as many criticisms are not taking into
account latest developments of the field, nor considering the use of
powerful tools provided by evolutionary computation for tackling some of
the difficult optimization problems of machine learning.
In my talk, I will present the field of evolutionary computation, with a
particular emphasis on how it can be used in the context of machine
learning. I will also present some methods of evolutionary computing
that are relying on machine learning. This will be illustrated by
various works done in my group at Université Laval, where we are
applying in synergy machine learning and evolutionary computation
techniques for solving complex engineering problems.
Presenter:
Christian Gagné
Professeur agrégé
Laboratoire de vision et systèmes numériques
Département de génie électrique et de génie informatique
Université Laval, Québec (Québec), Canada
http://vision.gel.ulaval.ca/~cgagne
christian.gagne@gel.ulaval.ca