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 agrégéUniversité Laval, Québec (Québec), Canada
http://vision.gel.ulaval.ca/~cgagne
christian.gagne@gel.ulaval.ca