Hello,

More of my philosophy about Bonita Montero and about my new proverb and about mathematics and about performance and reliability and about fluid intelligence and about Leadership and about efficiency and about reliability and about the digital identity and about personality and about vitamin C and about indians and about arabs and about my philosophy and more of my thoughts...

I am a white arab from Morocco, and i think i am smart since i have also
invented many scalable algorithms and algorithms..

So Bonita Montero has just asked if i am still alive, and i am responding that i am still in a good shape, and so that to know what are my new thoughts that i am posting in internet , here is is my last following thoughts about the genetic algorithm and about my new software project that is a more sophisticated StopWatch, and you have to read about them carefully in my following thoughts, and of course you can read all my new thoughts that i have grouped below and in the below web links:

I think that the low probability mutations in the genetic algorithm permits to explore , so you have to read carefully all my new thoughts below about the genetic algorithm so that you understand how the human genetics works by also using natural selection, but here is my new question to ChatGPT that explains more and read my below thoughts about genetic algorithm so that you understand much more:

And here is my new question to ChatGPT:

So from what i understand , the exploration in the genetic algorithm is using an approximation, since the high degree of exploration permits to find a better global minimum and after that we explore in this near better global minimum so that to make the probability of finding a better global minimum better, so this shows that the genetic algorithm search is a much better search than brute force search.

And ChatGPT has just answered me the following:

"Yes, you have captured the essence correctly. Exploration in optimization algorithms, including genetic algorithms (GA) , involves searching the solution space to discover potentially better solutions. The initial high degree of exploration allows for the discovery of diverse regions, including the global minimum.

During the exploration phase, the algorithms aim to approximate the global minimum by sampling different areas of the solution space. This broader search helps in avoiding premature convergence to suboptimal regions and increases the probability of finding the true global minimum.

Once a promising region or solution is identified, the algorithms transition into an exploitation phase. In this phase, they concentrate their search in the vicinity of the promising region to refine and improve the solutions.. By focusing the search around this area, the algorithms increase the probability of finding a better global minimum, as they are now exploring within a region known to contain good solutions.

This two-step process, starting with a higher degree of exploration and transitioning into exploitation around promising regions, allows the algorithms to balance the search for global optimality while refining the solutions found.

The exploration phase sets the foundation by exploring a wide range of the solution space and identifying potentially good regions or solutions. Subsequently, the exploitation phase intensifies the search in the vicinity of these promising areas to fine-tune the solutions and potentially discover even better solutions.

By combining these exploration and exploitation strategies in the genetic algorithm increase the likelihood of finding better global minima, improving the overall efficiency and effectiveness of the optimization process."

More of my philosophy about the evolution of genetics of humans and about the genetic algorithm and more of my thoughts..

The cost function of a neural network is in general neither convex nor concave, so in deep learning you can use evolutionary algorithms such as the genetic algorithm and such, so you have then to know that in such situations you have to loop in a number of iterations so that to find better solutions, so for example the genetics of humans has evolved in a such way , since i think that the great number of iterations with the crossover steps and the mutations and the selection of the process of evolution of genetics of humans that look like a genetic algorithm, is what made humans be so "optimized" by for example having a smart brain, and of course you have to read my following thoughts so that to understand the rest of the patterns that i have discovered with my fluid intelligence:

More of my philosophy about the Traveling Salesman Problem Using an Evolutionary Algorithm and more of my thoughts..

I invite you to look at the following interesting just new article
of Visual Studio Magazine of The Traveling Salesman Problem Using an Evolutionary Algorithm with C#:

https://visualstudiomagazine.com/articles/2022/12/20/traveling-salesman-problem.aspx

I think i am highly smart, and I have passed two certified IQ tests and i have scored above 115 IQ, and i mean that it is "above" 115 IQ, and i have just understood rapidly the above program of The Traveling Salesman Problem using an evolutionary algorithm(a genetic algorithm) with C#, and i think that i am discovering the most important patterns with my fluid intelligence in the above program of the Traveling Salesman Problem using the genetic algorithm, and it is that the "crossover" steps in the genetic algorithm exploit better solution, and it means that they exploit locally the better solution, and using "mutation(s)" in the genetic algorithm you explore far away from the locally, and if the exploration finds a better solution , the exploitation will try to find a better solution near the found solution of the exploration, so this way of the genetic algorithm to balance the explore and the exploit is what makes the genetic algorithm interesting, so you have to understand it correctly so that to understand the genetic algorithm.

I think i am highly smart, and I have passed two certified IQ tests and i have scored above 115 IQ, and i mean that it is "above" 115 IQ, so i am again discovering patterns with my fluid intelligence, and it is that the standard genetic algorithm has a problem, and it is that it can get stuck in a local optimum and have a premature convergence and the premature convergence of a genetic algorithm arises when the genes of some high rated individuals quickly attain to dominate the population, constraining it to converge to a local optimum. The premature convergence is generally due to the loss of diversity within the population, so i think that you have to solve this problem by using "probability", i mean that you have to divide the population of the genetic algorithm in many groups of population and do the crossover and mutations in each group, so this will lower much more the probability to a small probability of getting stuck in a local optimum and of having a premature convergence, so then i will invite you to look above at the just new article of Visual Studio Magazine of The Traveling Salesman Problem using an evolutionary algorithm with C#, and how it is not talking about all my patterns that i am discovering with my fluid intelligence, and it is not explaining as i am explaining the genetic algorithm.

So as you are noticing that in my new above thoughts, i am saying that the distribution of the population fights the premature convergence by lack of diversity, but why am i not saying a "good" distribution? since it is inherent that the population has to be well distributed so that the genetic algorithm explores correctly. And as you have just noticed that this thoughts are the thoughts of mine that i am discovering and sharing them with you.

I think i am highly smart, and I have passed two certified IQ tests and i have scored above 115 IQ, and i mean that it is "above" 115 IQ, so
as you have just noticed, i have just showed you how to avoid premature convergence by lack of diversity, read about it above, but i think i have to explain one more important thing about the genetic algorithm, and it is that when you start a genetic algorithm, you are using a population, so since the distribution of the population also fights against the premature convergence by lack of diversity, so then so that to lower the probability to a small probability of getting stuck in a local optimum by lack of diversity, you can rerun the genetic algorithm a number of times by using a new distribution of the population in every execution of the genetic algorithm and using a good size of the population, or you can use my above methodology so that to avoid it efficiently in a single execution.

So i think i am highly smart since I have passed two certified IQ tests and i have scored above 115 IQ, and i mean that it is "above" 115 IQ, so you have to understand the essence of measuring the time in computers ,
so for example C++ <chrono> library does not provide a direct way to retrieve the CPU frequency. The C++ <chrono> library primarily deals with time-related operations, such as measuring durations and performing time point calculations, but the best accuracy of C++ <chrono> library is in nanosecond, so it is not good, since you also need the accuracy in CPU cycles or ticks as is providing it my new StopWatch, so i invite you to study my new StopWatch so that to know how to implement a good StopWatch, so i invite you to read my below previous thoughts about the StopWatch so that
you understand my views:

So i think i am highly smart since I have passed two certified IQ tests and i have scored above 115 IQ, and i mean that it is "above" 115 IQ, so i am talking about my fluid intelligence in my below previous thoughts, but you have to notice that professionalism is also important, so you are noticing that i have just rapidly implemented a sophisticated StopWatch , and you can also notice my kind of professionalism, since i am rapidly discovering patterns with my fluid intelligence, and i am also professional since you are noticing the way that i am implementing it and the way i am learning you, so i think you have to be confident with my professionalism, since even if i have rapidly done it, you are clearly noticing the quality of the my new StopWatch, and of course i will document it correctly so that you know how to use it correctly and so that you know how to implement a good StopWatch, and of course i am supporting the x86 and x64 CPUs, and of course i can also support the ARM processors, but you have to read my below thoughts that explain my views on the ARM processors and about the ARM vs x86 and x64 CPUS, so i invite you to read my previous below thoughts so that
you understand my views about how to implement a good StopWatch:

On 7/19/2023 3:07 PM, Amine Moulay Ramdane wrote:
> Hello,
[...]

A weak memory model has a lot more opportunities for optimizations.

Hello,

More of my philosophy about my previous post to Bonita Montero...

So as you have just noticed that in my just previous post, i am repeating the
following sentence about me:

"I think i am highly smart since i have passed two certified IQ tests and i have scored above 115 IQ, and i mean that it is "above" 115 IQ"

So it is the truth, but i am not repeating it, since i have grouped my previous thoughts parts by parts in this one previous post, and some parts contained the above sentence of:

"I think i am highly smart, and I have passed two certified IQ tests and i have scored above 115 IQ, and i mean that it is "above" 115 IQ"

So you have to know that i am a gentleman type of person, and you can read all my previous thoughts on the previous post so that you know more about me.

Thank you,
Amine Moulay Ramdane.

Am 20.07.2023 um 00:28 schrieb Chris M. Thomasson:
> On 7/19/2023 3:07 PM, Amine Moulay Ramdane wrote:
>> Hello,
> [...]
>
> A weak memory model has a lot more opportunities for optimizations.

Do you really think you can argue with Amine ?