Due: Wednesday, April 11th.

• Read Resilient Individuals Improve Evolutionary Search and it's two original reviews: review 1 and review 2.
• Meet with a different faculty member in CS (not me) and take 5-10 minutes to ask them about their research. Before the meeting you should look at their webpage and/or look up some of their published papers to help you understand their research. All of the CS faculty should have office hours posted outside of their office or on their webpage.
• Starting with the population class we created in class (and available on the course schedule) add a select_worse() function to the class that selects based on the lowest fitness.
• Using the select(), select_worse(), copy(), and mutate() functions create a new function that is part of the population class that does the following: selects a high fitness individual, selects a low fitness individual, copies the high fitness individual over the low fitness individual, mutates the copy. Use a loop to call this function repeatedly (~100 times). Print what happens to the population's average fitness during this loop.

Turn in: A one paragaph summary of the research of the CS faculty member that you met with. The python code and sample output showing that all of the new functions work correctly and how the average fitness of the population changes over time.