AI Lab #4

Copy the GA files provided in the directory below to your home directory.
```
~meeden/Public/cs63/lab4-ga/
```
Make the executable program and try running it as follows:
```
ga -f test -s 50 -l 20 -g 500
```
This will run a GA with a population size of 50, a chromosome length of 20, a maximum number of generations of 500, and will write the results to files named test.GAmsg, test.GAavg, and test.GAbest. The GA is currently set to use the binary fitness function. Look through the output that is produced.
Edit the ga.c file. Find the test_fitness function, and modify it to use a different fitness function (either ones or zeros). Re-make the program, run it, and look through the new results.
Again edit the file. Find the check_schema flag and set it to 1. Re-make the program, run it, and look at the schema results.
Take some time to familiarize yourself with how the GA is implemented. You are welcome to modify this code in any way you see fit.

A description of the problem domain.
A detailed explanation of how you represented the problem as a bit string for the GA, which includes several examples.
A description of the fitness functions you considered and which turned out to be the most effective.
A summary of the parameter settings you used.
A table of results. This table should include at least 10 different runs using the same parameter settings, and should show the best fitness found, and at what generation it was found.
A discussion of the results. Was the GA successful?