This example shows the effects of some options for the genetic algorithm function ga. You create and change options by using the optimoptions function. Setting Up a Problem for ga ga searches for a minimum of a function using the genetic algorithm. For this example, use ga to minimize the fitness function shufcn, a real valued function of two variables. The first two output arguments returned by ga are x, the best point found, and Fval, the function value at the best point.

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The results and conclusions are my opinion and may or may not constitute applicable techniques of predicting the popular Jamaican Lottery Cash Pot game. The game is based on 36 balls being loaded into a chamber and one ball been selected at random from the grouping.

The game is ran four 4 times each day seven 7 days per week. Anecdotal Heuristics: While doing a little tongue and cheek research at my favorite barbershop, I stumbled upon some heuristics that are employed by most patrons who play the CP game.

After building up a sufficient dataset, they could then query a particular day and time; and with some simple arithmetic tally the most likely number to be played on that day and time. I was informed that this proved to be a very efficient way of telling which number was to be played next. Another popular heuristic involved pre-assigned symbols; these symbols were associated with each of the thirty six 36 numbers.

These two methods were the favorite amongst the players of Cash Pot. Select columns B and D then hit the import button; this should import only columns B and D, rename the imported matrix to cpInputs.

Import cpInputs and cpTargets into the NN data manager. Set Input data to cpInputs, Target data to cpTargets. Hit the create button to create the NN. Note: The newly created NN has two inputs, the first been the day of the week on which the [CP] is scheduled to be played and the second input the time of day that the [CP] is scheduled to played. It also has a hidden layer with 10 neurons with associated bias, and an output layer with 1 neuron and its associated bias. The output is a scalar double which represents the predicted winning number.

Results of training. Conclusions: My initial analysis of the results of the NN was not conclusive, maybe the parameters of the NN could be adjusted and the results compared to actual winning numbers.

However, even after doing so one may find that the outputs are still random and contain no discernible patterns, which should be the case for a supposedly random game of chance.


gatool + fuzzy logic

Jut Is there a reason you are trying to express the values in binary as opposed to just placing integer constraints with bounds? Direct link to this answer: If the user is using gatool then we need to know which release the user is using, as it must be a fairly old release. I would like to use gatool to optimize a discrete function whose analytical expression is not known, but whose values in some given points can be computed by means of a script. Based on your location, we recommend that you select: To find out more, including how to control cookies, see here: Sign in to comment.


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