| Amine Moulay Ramdane <aminer68@gmail.com>: Dec 28 02:40PM -0800 Hello, More of my philosophy about the best solutions and the genetic algorithms 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.. 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 notice that i am discovering the below patterns in the genetic algorithm with my fluid intelligence, and i have just discovered another pattern with my fluid intelligence in the genetic algorithm, and it is when you make bigger the size of the population of the genetic algorithm , the best solutions are improved with the mutations in this "bigger" population, so this "higher" the "probability" of finding more best solutions, so i think that it makes the genetic algorithm better. More of my philosophy about the accuracy of the genetic algorithm and about the genetic algorithm and more of my thoughts.. 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 have just tested the genetic algorithm on some applications, and i think that the genetic algorithm is by nature not suited for giving exact solutions, since it gives good approximations of the solution, but good approximations are still good for many applications, also about the cost function in the genetic algorithm or in artificial intelligence in general, so i predict that artificial intelligence like with deep learning and transformers and the like will not attain general artificial intelligence, since i say that the cost function that permits to guide artificial intelligence can not be implemented just with deep learning and transformers and the like so that to make general artificial intelligence, but i predict that we have to understand human consciousness", and it is human consciousness that will make the cost function in artificial intelligence really smart so that to permit general artificial intelligence, so then we have to understand the brain consciousness for that, but i think that artificial intelligence with deep learning and transformers and the like is also powerful since it will permit an accelerating returns that is so appreciable, so then i can logically infer that african countries have to be connected to internet with a computer and a smartphone, since i think that so that to adapt to the law of accelerating returns, people have to access internet and learn efficiently from internet, so for example read the following web page about the share of internet users in Africa as of January 2022, by country: https://www.statista.com/statistics/1124283/internet-penetration-in-africa-by-country/ So notice in the above web page how Morocco my arab country is the best country in Africa that has 84.1% of its people connected to internet and notice that the other arab country of Egypt is also good at that, since it has 79.1% of its people that are connected to internet, but i am noticing that arabs of north african countries are adapting much more efficiently since the majority of them are being well connected to internet, and thus i predict that they will adapt much more efficiently to law of accelerating returns, but notice in the above web page how many black african countries are really poorly connected to internet. Other than that i invite you to read my previous thoughts about the genetic algorithm so that you understand my views: More precision of my philosophy about the essence of the genetic algorithm and more of my thoughts.. So as you are noticing that in my new below 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, so reread all my thoughts below: 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 below, 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 below methodology so that to avoid it efficiently in a single execution. More of my philosophy about premature convergence of the genetic algorithm and more of my thoughts.. 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 below 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. 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 or PSO 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 precision 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. More of my philosophy about non-linear regression and about logic and about technology and more of my thoughts.. 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" , so i think that R-squared is invalid for non-linear regression, but i think that something that look like R-squared for non-linear regression is to use Relative standard error that is the standard deviation of the mean of the sample divide by the Estimate that is the mean of the sample, but if you calculate just the standard error of the estimate (Mean Square Error), it is not sufficient since you have to know what is the size of the standard error of the estimate relatively to the curve and its axes, so read my following thoughts so that to understand more: So the R-squared is invalid for non-linear regression, so you have to use the standard error of the estimate (Mean Square Error), and of course you have to calculate the Relative standard error that is the standard deviation of the mean of the sample divide by the Estimate that is the mean of the sample, and i think that the Relative standard Error is an important thing that brings more quality to the statistical calculations, and i will now talk to you more about my interesting software project for mathematics, so my new software project uses artificial intelligence to implement a generalized way with artificial intelligence using the software that permit to solve the non-linear "multiple" regression, and it is much more powerful than Levenberg–Marquardt algorithm , since i am implementing a smart algorithm using artificial intelligence that permits to avoid premature convergence, and it is also one of the most important thing, and it will also be much more scalable using multicores so that to search with artificial intelligence much faster the global optimum, so i am doing it this way so that to be professional and i will give you a tutorial that explains my algorithms that uses artificial intelligence so that you learn from them, and of course it will automatically calculate the above Standard error of the estimate and the Relative standard Error. More of my philosophy about non-linear regression and more.. I think i am really smart, and i have also just finished quickly the software implementation of Levenberg–Marquardt algorithm and of the Simplex algorithm to solve non-linear least squares problems, and i will soon implement a generalized way with artificial intelligence using the software that permit to solve the non-linear "multiple" regression, but i have also noticed that in mathematics you have to take care of the variability of the y in non-linear least squares problems so that to approximate, also the Levenberg–Marquardt algorithm (LMA or just LM) that i have just implemented , also known as the damped least-squares (DLS) method, is used to solve non-linear least squares problems. These minimization problems arise especially in least squares curve fitting. The Levenberg–Marquardt algorithm is used in many software applications for solving generic curve-fitting problems. The Levenberg–Marquardt algorithm was found to be an efficient, fast and robust method which also has a good global convergence property. For these reasons, It has been incorporated into many good commercial packages performing non-linear regression. But my way of implementing the non-linear "multiple" regression in the software will be much more powerful than Levenberg–Marquardt algorithm, and of course i will share with you many parts of my software project, so stay tuned ! More of my philosophy about the truth table of the logical implication and about automation and about artificial intelligence and more of my thoughts.. 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", and now i will ask a philosophical question of: What is a logical implication in mathematics ? So i think i have to discover patterns with my fluid intelligence in the following truth table of the logical implication: p q p -> q 0 0 1 0 1 1 1 0 0 1 1 1 Note that p and q are logical variables and the symbol -> is the logical implication. And here are the patterns that i am discovering with my fluid intelligence that permit to understand the logical implication in mathematics: So notice in the above truth table of the logical implication that p equal 0 can imply both q equal 0 and q equal 1, so for example it can model the following cases in reality: If it doesn't rain , so it can be that you can take or not your umbrella, so the pattern is that you can take your umbrella since it can be that another logical variable can be that it can rain in the future, so you have to take your umbrella, so as you notice that it permits to model cases of the reality , and it is the same for the case in the above truth table of the implication of if p equal 1, it imply that q equal 0 , since the implication is not causation, but p equal 1 means for example that it rains in the present, so even if there is another logical variable that says that it will not rain in the future, so you have to take your umbrella, and it is why in the above truth table p equal 1 imply q equal 1 is false, so then of course i say that the truth table of the implication permits to model the case of causation, and it is why it is working. More of my philosophy about objective truth and subjective truth and more of my thoughts.. Today i will use my fluid intelligence so that to explain more the way of logic, and i will discover patterns with my fluid intelligence so that to explain the way of logic, so i will start by asking the following philosophical question: What is objective truth and what is subjective truth ? So for example when we look at the the following equality: a + a = 2*a, so it is objective truth, since it can be made an acceptable general truth, so then i can say that objective truth is a truth that can be made an acceptable general truth, so then subjective truth is a truth that can not be made acceptable general truth, like saying that Jeff Bezos is the best human among humans is a subjective truth. So i can say that we are in mathematics also using the rules of logic so that to logically prove that a theorem or the like is truth or not, so notice the following truth table of the logical implication: p q p -> q 0 0 1 0 1 1 1 0 0 1 1 1 Note that p and q are logical variables and the symbol -> is the logical implication. The above truth table of the logical implication permits us to logically infer a rule in mathematics that is so important in logic and it is the following: (p implies q) is equivalent to ((not p) or q) And of course we are using this rule in logical proofs since we are modeling with all the logical truth table of the logical implication and this includes the case of the causation in |
| Amine Moulay Ramdane <aminer68@gmail.com>: Dec 28 09:54AM -0800 Hello, More of my philosophy about the accuracy of the genetic algorithm and about the genetic algorithm 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.. 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 have just tested the genetic algorithm on some applications, and i think that the genetic algorithm is by nature not suited for giving exact solutions, since it gives good approximations of the solution, but good approximations are still good for many applications, also about the cost function in the genetic algorithm or in artificial intelligence in general, so i predict that artificial intelligence like with deep learning and transformers and the like will not attain general artificial intelligence, since i say that the cost function that permits to guide artificial intelligence can not be implemented just with deep learning and transformers and the like so that to make general artificial intelligence, but i predict that we have to understand human consciousness", and it is human consciousness that will make the cost function in artificial intelligence really smart so that to permit general artificial intelligence, so then we have to understand the brain consciousness for that, but i think that artificial intelligence with deep learning and transformers and the like is also powerful since it will permit an accelerating returns that is so appreciable, so then i can logically infer that african countries have to be connected to internet with a computer and a smartphone, since i think that so that to adapt to the law of accelerating returns, people have to access internet and learn efficiently from internet, so for example read the following web page about the share of internet users in Africa as of January 2022, by country: https://www.statista.com/statistics/1124283/internet-penetration-in-africa-by-country/ So notice in the above web page how Morocco my arab country is the best country in Africa that has 84.1% of its people connected to internet and notice that the other arab country of Egypt is also good at that, since it has 79.1% of its people that are connected to internet, but i am noticing that arabs of north african countries are adapting much more efficiently since the majority of them are being well connected to internet, and thus i predict that they will adapt much more efficiently to law of accelerating returns, but notice in the above web page how many black african countries are really poorly connected to internet. Other than that i invite you to read my previous thoughts about the genetic algorithm so that you understand my views: More precision of my philosophy about the essence of the genetic algorithm and more of my thoughts.. So as you are noticing that in my new below 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, so reread all my thoughts below: 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 below, 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 below methodology so that to avoid it efficiently in a single execution. More of my philosophy about premature convergence of the genetic algorithm and more of my thoughts.. 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 below 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. 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 or PSO 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 precision 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. More of my philosophy about non-linear regression and about logic and about technology and more of my thoughts.. 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" , so i think that R-squared is invalid for non-linear regression, but i think that something that look like R-squared for non-linear regression is to use Relative standard error that is the standard deviation of the mean of the sample divide by the Estimate that is the mean of the sample, but if you calculate just the standard error of the estimate (Mean Square Error), it is not sufficient since you have to know what is the size of the standard error of the estimate relatively to the curve and its axes, so read my following thoughts so that to understand more: So the R-squared is invalid for non-linear regression, so you have to use the standard error of the estimate (Mean Square Error), and of course you have to calculate the Relative standard error that is the standard deviation of the mean of the sample divide by the Estimate that is the mean of the sample, and i think that the Relative standard Error is an important thing that brings more quality to the statistical calculations, and i will now talk to you more about my interesting software project for mathematics, so my new software project uses artificial intelligence to implement a generalized way with artificial intelligence using the software that permit to solve the non-linear "multiple" regression, and it is much more powerful than Levenberg–Marquardt algorithm , since i am implementing a smart algorithm using artificial intelligence that permits to avoid premature convergence, and it is also one of the most important thing, and it will also be much more scalable using multicores so that to search with artificial intelligence much faster the global optimum, so i am doing it this way so that to be professional and i will give you a tutorial that explains my algorithms that uses artificial intelligence so that you learn from them, and of course it will automatically calculate the above Standard error of the estimate and the Relative standard Error. More of my philosophy about non-linear regression and more.. I think i am really smart, and i have also just finished quickly the software implementation of Levenberg–Marquardt algorithm and of the Simplex algorithm to solve non-linear least squares problems, and i will soon implement a generalized way with artificial intelligence using the software that permit to solve the non-linear "multiple" regression, but i have also noticed that in mathematics you have to take care of the variability of the y in non-linear least squares problems so that to approximate, also the Levenberg–Marquardt algorithm (LMA or just LM) that i have just implemented , also known as the damped least-squares (DLS) method, is used to solve non-linear least squares problems. These minimization problems arise especially in least squares curve fitting. The Levenberg–Marquardt algorithm is used in many software applications for solving generic curve-fitting problems. The Levenberg–Marquardt algorithm was found to be an efficient, fast and robust method which also has a good global convergence property. For these reasons, It has been incorporated into many good commercial packages performing non-linear regression. But my way of implementing the non-linear "multiple" regression in the software will be much more powerful than Levenberg–Marquardt algorithm, and of course i will share with you many parts of my software project, so stay tuned ! More of my philosophy about the truth table of the logical implication and about automation and about artificial intelligence and more of my thoughts.. 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", and now i will ask a philosophical question of: What is a logical implication in mathematics ? So i think i have to discover patterns with my fluid intelligence in the following truth table of the logical implication: p q p -> q 0 0 1 0 1 1 1 0 0 1 1 1 Note that p and q are logical variables and the symbol -> is the logical implication. And here are the patterns that i am discovering with my fluid intelligence that permit to understand the logical implication in mathematics: So notice in the above truth table of the logical implication that p equal 0 can imply both q equal 0 and q equal 1, so for example it can model the following cases in reality: If it doesn't rain , so it can be that you can take or not your umbrella, so the pattern is that you can take your umbrella since it can be that another logical variable can be that it can rain in the future, so you have to take your umbrella, so as you notice that it permits to model cases of the reality , and it is the same for the case in the above truth table of the implication of if p equal 1, it imply that q equal 0 , since the implication is not causation, but p equal 1 means for example that it rains in the present, so even if there is another logical variable that says that it will not rain in the future, so you have to take your umbrella, and it is why in the above truth table p equal 1 imply q equal 1 is false, so then of course i say that the truth table of the implication permits to model the case of causation, and it is why it is working. More of my philosophy about objective truth and subjective truth and more of my thoughts.. Today i will use my fluid intelligence so that to explain more the way of logic, and i will discover patterns with my fluid intelligence so that to explain the way of logic, so i will start by asking the following philosophical question: What is objective truth and what is subjective truth ? So for example when we look at the the following equality: a + a = 2*a, so it is objective truth, since it can be made an acceptable general truth, so then i can say that objective truth is a truth that can be made an acceptable general truth, so then subjective truth is a truth that can not be made acceptable general truth, like saying that Jeff Bezos is the best human among humans is a subjective truth. So i can say that we are in mathematics also using the rules of logic so that to logically prove that a theorem or the like is truth or not, so notice the following truth table of the logical implication: p q p -> q 0 0 1 0 1 1 1 0 0 1 1 1 Note that p and q are logical variables and the symbol -> is the logical implication. The above truth table of the logical implication permits us to logically infer a rule in mathematics that is so important in logic and it is the following: (p implies q) is equivalent to ((not p) or q) And of course we are using this rule in logical proofs since we are modeling with all the logical truth table of the logical implication and this includes the case of the causation in it, so it is why it is working. And i think that the above rule is the most important rule that permits in mathematics to prove like the following kind of logical proofs: (p -> q) is equivalent to ((not(q) -> not(p)) Note: the symbol -> means implies and p and q are logical variables. or (not(p) -> 0) is equivalent to p And for fuzzy logic, here is the generalized form(that includes fuzzy logic) for the three operators AND,OR,NOT: x AND y is equivalent to min(x,y) x OR y is equivalent to max(x,y) NOT(x) is equivalent to (1 - x) So now you are understanding that the medias like CNN have to be objective by seeking the attain the objective truth so that democracy works correctly. |
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