Evolutionary Robotics

The course offers an introduction to evolutionary computation, describes in detail the most widely used algorithms, and compares evolutionary algorithms to reinforcement learning. It also describes applications of evolutionary algorithms to neural networks, neural controllers of mobile robots, and body-brain co-design. Students will carry out exercises with selected evolutionary algorithms, and then engage in gradually more complex competitions to evolve neural controllers of mobile robots in physics-based simulations. Finally, students will carry out a project in simulation on the co-evolution of body and brains of robots. The course consists of ex-cathedra lectures, software exercises, and competitions in Python.

Credits: 4

Room: BS 160

Thursday, 09:15-13:00

Final exam: Wednesday 01.07.2026 from 09h15 to 10h15 (room CM 1 106) 

Bring your student ID and an ink pen (not a pencil). Books, notes, personal devices are not allowed. Students with special arrangements from SAC, please email Fuda.vanDiggelen@epfl.ch for confirmation before the exam. Grade: 50% written exam (Multiple Choice Questions), 50% project presentation

Suggested readings: 

• Eiben, A. E. and Smith, J. E. (2003, 2015) Introduction to Evolutionary Computing. Berlin: Springer Verlag
• Floreano, D. and Mattiussi, C. (2008, 2023) Bio-inspired Artificial Intelligence. Cambridge, MA: MIT Press. 
  
• Risi, S., Tang, Y., Ha, D. and Miikkulainen, R. (2025) Neuroevolution. Cambridge, MA: MIT Press. 
  
• In addition, recent research articles are provided on Moodle.