Thank you. The reason why you save them is because you want to keep track of solutions because at the end of your search, if you do not converge to a good solution, you can look back through all of your solutions and pick the best solution out of all the ones that you stored. And we're going to also save the best for each generation because in each generation you end up getting a population of 100 right? So with a genetic algorithm, you have population or solutions everywhere, and you try to find the solutions that would give you the minimum answered the global minimum again. So now we have let's call them prize one is equal to object to function. But instead, now, this the 1st 15 represent, uh, learning great. Now let's we started them on top of each other again. After representing each chromosome the right way to serve to search the space, next is to calculate the fitness value of each individual. This would become warrior one. Crossover in GA generates new generation the same as natural mutation. You have billions off new runs on these new runs are connected to each other by accents. And why so? ics. This would be the better, and the rest will be the chromosome, so into zero index one index to so index to until the rest would be the final solution. And and all of these nodes are connected to each other through through edges on and on each edge, There exists a weight now. So based on the LRs element off the unless that would be decoded because the end list it changes. And these notes are connected to each other through edges, which is like the human brain with axe on. But since we only want the 1st 2nd 6 and eight and nine feature because it sources zero So this is 1268 and nine features. So I'll pause the video now and then. Just a very important and quick note. Probability is based on the fitness value off that individual. << The reason is because it's infinite, so you have to specify a range that you want your search to be in. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. Next variation operator is mutation. Before getting into the details of how GA works, we can get an overall idea about evolutionary algorithms (EAs). So this is what we're going to work with, and I won't go below here for Warrior one, the empty list we need to create. Perceptive neural network in order to, uh, these are the independent variables and these two are dependent variables. But when you go back, you packed back propagate, and you are just the weights. Separable. x��=k�9r� �?�K ��i7��\�0�w7���|>'��}�3�XY�hV�x�A~|X�7�d����GRw�,��"�|~q8m?�/O������鴾���Z|x�~�����m��]_ooק����|����ŋ�O�#��آ�]������O��5�����׷�3�. And then at Inducts One, which is this column, because before that, we said generations or this index would be the editor. If it is less than the probability of your mutation, you mutate. Let's say this is your error function and this is your weight. Keep it at the default, which is Adam. But when both gamma and see are set to, ah, low number, you can see how it differs from here to here. Optimize a prompter gamma. Mutation varies based on the chromosome representation but it is up to you to decide how to apply mutation. Sometimes the offspring takes half of its genes from one parent and the other half from the other parent and sometimes such percent changes. Thank you. And then now we need to get again. Warrior Warrior one. So we have these with stack them on top of each other, which has the generation. So I'll see in the next lecture. Okay. And it'll keep adding and just adding, If it's one and this is how you end up with this upset off features based on the gene you choose, let's do this zero. Multilayer Perceptron Neural Network Optimization #2: Welcome back for now here just like we did with support Vector Machine where we did see on gobble we did see and Gamma where the fist 1st 15 represented see and the 2nd 15 represented gamma Same thing here we have learning great momentum where this one represents learning rate and the other represented momentum on the minimum value with learning great would take his 0.0 on and the maximum is 0.3 The reason why I put it 0.3 not more is because if you remember, we said we don't want overshoot on miss the local or global There is a continual problem No way we can hit the global Optima but we don't want overshoot and miss a local a good local minima Same thing with the momentum should be between zero and one But this is how we decoded If you remember when we did zero to the to to borrow zero times this plus two to the power of warm times that plus two to the power of two times as and so on.


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