CS 5970 Programming Assignment 2 — Genetic Algorithms
Due Monday, March 11, 2013
NOTE: The hardcopies of the parts of this assignment are due at
the beginning of the class period. This means that if you are
even a minute late, you lose 20%. If you are worried about
potentially being late, turn in your assignments ahead of
time. Do this by submitting them to me during office hours or by
sliding them under my office door. Electronic copies are due by 4:00
pm on the due date. Submit them through D2L before the time they are
due. Do not send assignments to me through email or leave them in my
departmental mail box.
1. Motivation
The required components of evolutionary computation are replicators,
replication, and a selection mechanism, and we require that the
replication be high fidelity. When we go to create a particular
evolutionary computation implementation, we need to decide on
particulars for each of the requirements as well as others. Some of
the questions we need to answer to create our implementations are:
- What will the replicators be? Bits? Floats? Strings?
Something else?
- How will they be organized? In an array? Multiple arrays? A
tree? Etc.
- Etc.
All of these decisions need to be made in the context of the
particular problem to which evolutionary computation will be applied
(for example, the replicators in combination must be able to represent
solutions to the problem in question). These decisions will also
effect the unfolding of the evolutionary process (e.g., how quickly it
converges to a solution).
2. Goals
The goals of this assignment are:
- to give you baseline experience with implementing a evolutionary
computation system,
- to give you experience with genetic algorithms in
particular,
- to familiarize you with some of the options facing evolutionary
computation implementors,
- to give you experience with observing the evolutionary
process.
3. Assignment Overview
You will design, program, and run a simple genetic algorithm in
which each individual has a single chromosome of 60 bits to carry out
the following simple tasks:
- search for the optimum in the onemax problem,
- search for the optimum in the following problem which we will
call “plateau-max” for this assignment:
- if the number of ones is less than or equal to 20, then the fitness
is equal to the number of ones,
- if the number of ones is between 20 and 40 exclusive, then the
fitness is equal to 20,
- if the number of ones is greater than or equal to 40, then the
fitness is equal to the number of ones minus 20.
You will also turn in written material regarding the design and
implementation choices you made regarding the GA and an analysis of the
results you will collected from your runs.
4. Assignment Details
Carry out the following steps. Underlined steps require a
written response, those in code
require you to write
software, and those in italics require you to collect data.
Written responses, code
, and data will be
turned in for grading
- Consider the choices one needs to make regarding the design
and implementation of any evolutionary computation system.
- List the choices that need to be made when designing an
evolutionary computation system that have already been made for you
in this assignment. You should be able to list at least
four.
- For each of these choices, list which option I chose for you
in making this assignment.
- List the choices you need to make regarding the design of your
GA. (Note, these do not include purely implementation choices such
as programming language.) You should be able to list at least
five.
- Choose an option that seems reasonable to you for each of
these design choices and explain why it seems reasonable to
you. If you do not have a good reason for your chosen option,
say so.
Implement your GA.
- Now that you have implemented your GA, you are likely to have
recognized more choices that you needed to make along the way.
List the choices you needed to make regarding the design of your
GA. Including the choices you listed previously, your list
should now contain at least ten choices.
- For each of
these design choices, list the option you chose, and explain why it
seems reasonable to you. If you do not have a good reason for
your chosen option, say so.
- Answer the following questions about data collection,
reporting, and conclusions so that you are ensured of collecting
the appropriate data. Attempt to justify your answers to these
questions.
As you do so, keep in mind that what you want is a data set that
allows you to understand the workings of your GA as an optimization
tool and you want to report the minimum amount that allows your
reader to thoroughly understand what you have learned. Note that you
may lack a justification for your answers to some of these questions
at this time. That is acceptable since this is your first assignment
in this course. However, you should keep all of these questions in
your mind as the course progresses and be able to give good,
justified answers to similar questions for later assignments.
- How many times will you run your GA? Once? Ten times? Twenty
times? 100 times?
- If you run your GA more than once, what will you change from
run to run? What will you keep the same?
- What data will you collect? Performance data such as success
or failure at reaching the global maximum or fitness at every time
step? (What constitutes a time step in your GA?) Population
statistics such as diversity of the population? (How would you
measure diversity?)
- What data will you report? Everything? Best performance?
Worst performance? Averages? If averages, what will be averaged
together? Average population fitness at a given time step during
a single run? Averages over multiple runs?
- How will you report this data? Text? Numbers? Graphs? What
form will these take?
- What conclusions will you be able to draw from your results?
- Run your GA on onemax.
- Report your results and conclusions regarding the application
of your GA to onemax.
- Run your GA on plateau-max.
- Report your results and conclusions regarding the application
of your GA to plateau-max.
- Compare the performance of your GA on onemax and
plateau-max and report your conclusions.
5. What to Turn In
- Write Up
- You will turn in both a hard copy and an electronic copy of your
write up. Your write-up should be a coherent document that covers
all of the underlined steps from the assignment above. Note
that selected data in a digested form (such as tables or graphs)
should be included in your writeup; however, your raw data should not
be included here.
- Code
- You will turn in both a hard copy and an electronic copy of your
code. You will turn in the source code you have written for this GA.
Your source code should be well
structured and well commented. It should conform to good coding
standards (e.g., no memory leaks).
- Data
- You will turn in just an electronic copy (no hard copy) of your
data. This may be in a single file or multiple files. You will also
need to include a brief writeup on how the data is organized.
6. Notes on this Assignment
You may write your program from scratch or may start from programs for
which the source code is freely available (such as on the web or from
friends or student organizations). If you do not start from scratch, you
must give a complete and accurate accounting of where all of your
code came from and indicate which parts are original or changed, and which
you got from which other source. Similarly, for the written components of
this assignment you may follow the format or content of other written works
but you must give a complete and accurate accounting of who deserves
credit for all parts of your documents. You will be graded on the
contribution you make to your project; in other words, to earn the points
possible for this assignment, you must make your own substantial
contributions to the completion of this assignment above and beyond what
you obtain from others. Failure to give credit where credit is due is
academic fraud and will be dealt with accordingly. Please see the University's
web pages on academic integrity.