Game theory and algorithms

Lecturer(s): Joël PERRET LIAUDET, Philippe MICHEL
Course ⋅ 14 hTC ⋅ 4 hStudy ⋅ 10 h

Objectives

In this course, we show how to model some complex problems encountered in various domains (biology, politics, economics, design, ... ) by dealing with non-standard optimization algorithms (heuristics, meta-heuristics) and game theory. On simple cases, we will illustrate these resolution processes.

Keywords

optimization algorithm, heuristics, game theory.

Programme

Complexity / Heuristics / Simulated annealing / Genetic algorithms / Ant system / Particule swarm optimization Game Theory

Learning Outcomes

  • - solve applied optimization problems - modeling and application viaheuristic method - modeling and application via game theory

Assesment

Final mark = 50% Knowledge + 50% Know-how Knowledge = final exam Know-how = continuous assessment