Overview_07.ipynb

Kernel: Python 3 (system-wide)

# execute to import notebook styling for tables and width etc.
from IPython.core.display import HTML
import urllib.request
response = urllib.request.urlopen('https://raw.githubusercontent.com/DataScienceUWL/DS775v2/master/ds755.css')
HTML(response.read().decode("utf-8"));

Overview 07: Genetic Algorithms

Topics

• Genetic Algorithms and a custom utility module.

Learning Outcomes

The student will be able to:

• use genetic algorithms to identify good solutions to complex optimization problems

• recognize that stochastic optimization can identify good solutions that are necessarily the best solutions

Student "To Do" Checklist

• Reading

  • 14.1, 14.3, 14.4, and 14.5 in Hillier and Liebermann.

• Work your way through the Jupyter notebook called Lesson_07.ipynb. This is the main presentation and replaces the Storybook presentations used in our other courses. Use the self-assessments to measure your understanding.

• Complete the problems in Homework_07.ipynb in CoCalc prior to the due date.

Use Piazza to ask questions when you have them and be sure to check Piazza regularly so you don't miss out on any good Q & A or other discussions.

Supplemental Materials

We have two supplements for you this week:

• Lesson_07_Load_Balancing: This notebook uses a Load Balancing problem to do a deep dive into greedy local search, simulated annealing, genetic algorithms using DEAP. The DEAP package is the underlying package that is used in our wrapper function for genetic algorithms. You don't have to understand the inner workings of DEAP. But, if you are curious, this deep dive lays out how to use the various components of deap.

• Lesson_07_Load_Balancing_With_Constraints: This notebook takes the load balancing example one step further and adds constraints.