CS 5970 Programming Assignment 2 — Genetic Algorithms

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:

1. search for the optimum in the onemax problem,
2. 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

1. Consider the choices one needs to make regarding the design and implementation of any evolutionary computation system.
2. 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.
3. For each of these choices, list which option I chose for you in making this assignment.
4. 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.
5. 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.
6. Implement your GA.
7. 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.
8. 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.
9. 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?
10. Run your GA on onemax.
11. Report your results and conclusions regarding the application of your GA to onemax.
12. Run your GA on plateau-max.
13. Report your results and conclusions regarding the application of your GA to plateau-max.
14. 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.