|| In this project, students learn to use genetic algorithms to
solve three different sets of problems. They develop techniques for
selecting and crossing over populations, and learn how the mixin
design pattern can be used to easily evaluate a variety of
different crossover, selection, and elitism choices.
|| Genetic Algorithms, search, constraints
|| Intro AI
|| Medium. I give students two weeks to complete this.
|| Gives students a chance to easily apply GAs to non-trivial
problems and easily experiment with the wide variety of design
choices. Encourages them to develop a scientific approach to
parameter selection and algorithm evaluation. Illustrates the use of
mixins to easily modify an application.
|| Students must use Python.
|| Students should be familiar with search. I give this
about one month into the course. Students must be comfortable with
Python and OO programming.
|| The code is constructed so that it is very easy to add other
problems, such as N-queens or map coloring. Interested students can
also further explore selection, mutation and crossover operators and
perform experiments to evaluate their effectiveness. Depending on
the time available and skill sets of the students, instructors might
want to focus on a subset of this assignment.