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Hello and welcome to this lecture, where we are going to solve the flight schedule problem now using

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Melrose Library.

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It's quite easier than the library.

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First, we need to create that fitness function.

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Melrose Dot's custom fitness and here we will, sans fitness functions and roles we define as the before

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let's run the schools to create the variable.

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Now we need to create the problem variable and Melrose dots.

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This gratz obesity because we have a problem composed only of indicators, the length of the solution

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equals 12 because there are six people and each pupil has two flights.

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Then we need to specify the fitness maximize equals flows when we want to run a minimization problem.

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We need to specify maximize equals falls and maks vowel equals stan because we want to generate the

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numbers from zero to nine, which indicates the flights.

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So we both stand here.

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Let's create the problem variable.

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There's a table here, run again.

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And finally, let's create based solution and best fitness equals and.

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Let's call genetic algorithm.

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And though just sans the problem population size five hundred and mutation probability zero point three

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the same parameters we use before we can now see the values based, solution based fitness.

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Run this code.

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We can see that we have worst results when comparing to the library so we can try to run needs more

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times to see if we will get bad results every time we run.

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We have different results and to visualize this schedule, we can just brands ours, schedule and our

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will sans the best solution.

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Run this code and we can take a look at the flights.

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The value is quite bigger than using the library, so it seems that this order library performs bad

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there in this particular problem.

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And that's all for this lecture.

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See you next time.