Elective Synthesis

This elective course is a synthesis of the concepts of evolutionary computation. I want to document the core principles I’ve gained through this elective so that I can go back to them later if needed through my different projects.

Why this “Ressources” section ?

I have decided to document the corner stones of this course and of the different paper that I read for two main reasons :

  1. Reinforcement Learning : Explaining complex concepts is for me the best way to master them.
  2. Research base : These summaries will serve as a starting point for my own projects.

Key Takeaways

The approach chosen by Prof. Dennis Wilson allowed us to navigate through different strategies and algorythms to solve different complex problems :

  • Genetic Algorythms (GA) & Evolutionary Strategies (ES) : Here, we understand the fundamental mechanics of selection, crossover and mutation to explore the parameters space.
  • Neuroevolution : Here, we study the connection between evolutionnary algorythms and neural networks.
  • Multi-Objective Evolution : Learning how to optimize multiples and maybe competing objectives at the same time.
  • Genetic Programming : Here, we evolve actual computer programs or mathematical expressions.