Evolutionary computation and search

Many classes would group evolutionary computation under learning, for good reason.  It really is an adaptive method, which is key to learning.  However, the book groups it under real-world & local search, which is a place where evolutionary computation is often applied! For the sake of being consistent, I am keeping it in the local & real-world search methods but, keep in mind, this is also considered a machine learning algorithm (we will jump into ML more in a few modules!) 

One other note:  there is an entire class at OU focused on evolutionary computation! This topic is intended as just a quick overview.  If you find yourself super interested in it, consider taking the class!

• Note: All of the videos below have their slides in one pdf file

Overview of Evolutionary computation and genetic algorithms

• Reading
  • Read Section the rest of section 4.1
• Watch the overview video 

Genes and Chromosomes

How do we represent a gene and a chromosome in software or hardware?  

• Watch the video on genes and chromosomes

Fitness Functions and Selection Mechanisms

How do we decided which potential solutions should reproduce or be propagated in the next generation?

• Watch the video on fitness functions and selection mechanisms

Crossover and Mutation

How do we take two potential solutions and create a new one?

• Watch the video on crossover and mutation

Assignment

• Complete the exercise on evolutionary computation
• Easter egg: Post in #random your favorite amazing evolutionary creation from the natural world. Just pick something fun and creative, for example: do you love hedgehogs? Why did evolution make them the way they are? Post a photo or a meme of one and a thought on why it evolved that way.  

Next topic

• Next we will discuss another approach on how to handle uncertainty