Performed another experiment, with Genetic Algorithms.

When working with Genetic Algorithms, I know of essentially two types. One is an example, where a mutation algorithm generates a unit, which is itself an algorithm. A Windows-based program which used to work in this way, a long time ago, was named ‘Discipulus‘.

Another type of Genetic Algorithm which can exist, which can also be described as an ‘Evolutionary Programming’ example, is one which generates a unit, which is really just an arbitrary array of data, for which an externally-defined program must determine a fitness-level, so that the mutation algorithm can try to find units, which achieve greatest possible fitness. An example of such a system, which is still maintained today, is named ‘µGP3‘.

I would guess that something like ‘µGP3′ is more useful in Engineering, where a deterministic approach can be taken to determine how well a hypothetical machine would work, which was tweaked by the Evolved Data-Set, but according to a set of rules, which is not known to have an inverse.

‘Discipulus’ might be of greater use in AI, where the Genetic Algorithm is assumed to take a range of input parameters, and is required either to take an action based on those, or to arrive at an interpretation of those parameters, for which the AI was trained using numerous examples of input-value-sets, and for which a most-accurate result is known for each (training) set of simultaneous input-values. In the case of ‘Discipulus’, there exist two types of training exercises: Approximation, or Classification. And a real-world example where such a form would be useful, is in the computerized recognition of faces. Or of shapes, from other sorts of images.

Actually, I think that the way facial recognition works in practice today is, that a 2D Fourier Transform is computed of a rectangle, the dimensions of which in pixels have been tweaked, but in such a way that the conformity of the Fourier Transform to known Fourier Transforms pretty well guarantees that a given face is to be recognized.

But other examples may exist, in which the relationship between Input variables and Output Values is essentially of an initially-unknown nature. And then, even if we might not want to embed an actual GA into our AI, the use of GAs may provide some insight, as to how Input Values are in fact related to Output Values – through Human Interpretation of the GAs which result.

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The Cumulative Effect, Of Adding Many Random Numbers

The question must have crossed many people’s minds, of what the cumulative effect is, if they take the same calculated risk many times, i.e., if they add a series of numbers, each of which is random, and for the sake of argument, if each numbers has the same standard deviation.

The formal answer to that question is explained in This WiKiPedia Article. What the article states, is that ‘If two independently-random numbers are added, their expected values are added, as well as their variance, to give the expected value and the variance of the sum.’

But, what I already know, is that standard deviation is actually the square root of variance. Conversely, variance is already standard deviation squared. Therefore, the problem could be such, that the standard deviation of the individual numbers is known in advance, but that (n) random numbers are to be added. And then, because it is the square root of variance, the standard deviation of the sum will increase, as the square root of (n), times whatever the standard deviation of any one number in the series was.

This realization should be important to any people, who have a gambling problem, because people may have a tendency to think, that if they had ‘bad luck’ at a gambling table, ‘future good luck’ will come, to cancel out the bad luck they’ve already experienced. This is generally untrue, because as (n) increases, the square root of (n) will also just take the sum – of individual bets if the reader wishes – further and further away, from the expected value, because the square root of (n) will still increase. On average!

But, if we are to consider the case of gambling, then we must also take into account the expected value, which is just the average return of one bet. In the real-world case of gambling, this value is biased against the player, and earns the gambling establishment its profit. Well, according to what I wrote above, this will continue to increase linearly.

Now, the question which may come to mind next would be, what effect such a summation of data has on averages. And the answer lies in the fact that the square root of (n), is a half-power of (n). A full power of (n) would grow linearly with (n), while the zero-power of (n), would just stay constant.

And so the effect of summing many random numbers will first of all be, that the maximum and the minimum result theoretically possible, will be (n) times as far apart as they were for any one random number. This reflects the possibility, that ‘if (n) dice were rolled’, they could theoretically all come up as the maximum value possible, or all come up as the minimum value possible. And what this does to the graph of the distribution, is it initially makes the domain of the distribution curve linearly wider, along the x-axis, as a function of (n) – as the first power of (n).

(Updated 05/16/2018 … )

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