Hello and welcome to this lecture, where we are going to solve the flight schedule problem now using
Melrose Library.
It's quite easier than the library.
First, we need to create that fitness function.
Melrose Dot's custom fitness and here we will, sans fitness functions and roles we define as the before
let's run the schools to create the variable.
Now we need to create the problem variable and Melrose dots.
This gratz obesity because we have a problem composed only of indicators, the length of the solution
equals 12 because there are six people and each pupil has two flights.
Then we need to specify the fitness maximize equals flows when we want to run a minimization problem.
We need to specify maximize equals falls and maks vowel equals stan because we want to generate the
numbers from zero to nine, which indicates the flights.
So we both stand here.
Let's create the problem variable.
There's a table here, run again.
And finally, let's create based solution and best fitness equals and.
Let's call genetic algorithm.
And though just sans the problem population size five hundred and mutation probability zero point three
the same parameters we use before we can now see the values based, solution based fitness.
Run this code.
We can see that we have worst results when comparing to the library so we can try to run needs more
times to see if we will get bad results every time we run.
We have different results and to visualize this schedule, we can just brands ours, schedule and our
will sans the best solution.
Run this code and we can take a look at the flights.
The value is quite bigger than using the library, so it seems that this order library performs bad
there in this particular problem.
And that's all for this lecture.
See you next time.