| aminer68@gmail.com: Aug 11 02:57PM -0700 Hello, What is being smart like a genius ? I am a white arab, and i think i am smart like a genius, since i have invented many scalable algorithms and i am still inventing other scalable algorithms, not only that but i am explaining to you what is smartness and what is consciousness and what is self-awareness, since i am smart like a genius and i am understanding them with my smartness, so i will soon write an article that explains them, also i am able to understand easily artificial intelligence since i am smart like genius, and you will soon notice it more when i will show you my next Open source software projects that implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO. Read my previous thoughts: More smartness about what is artificial intelligence.. I am a white arab, and i think i am smart since i have invented many scalable algorithms, and when you are smart you will easily understand artificial intelligence, this is why i am finding artificial intelligence easy to learn, i think to be able to understand artificial intelligence you have to understand reasoning with energy minimization, like with PSO(Particle Swarm Optimization), but you have to be smart since the Population based algorithm has to guarantee the optimal convergence, and this is why i am learning you how to do it(read below), i think that GA(genetic algorithm) is good for teaching it, but GA(genetic algorithm) doesn't guarantee the optimal convergence, and after learning how to do reasoning with energy minimization in artificial intelligence, you have to understand what is transfer learning in artificial intelligence with PathNet or such, this transfer learning permits to train faster and require less labeled data, also PathNET is much more powerful since also it is higher level abstraction in artificial intelligence.. Read about it here: https://mattturck.com/frontierai/ And read about PathNet here: https://medium.com/@thoszymkowiak/deepmind-just-published-a-mind-blowing-paper-pathnet-f72b1ed38d46 Read my previous thoughts: More about artificial intelligence.. You have to understand me, i am going really fast in understanding artificial intelligence, since i think i am a white arab that is smart since i have invented many scalable algorithms, but i think one of the most important part in artificial intelligence is reasoning with energy minimization, it is the one that i am working on right now, see the following video to understand more about it: Yann LeCun: Can Neural Networks Reason? https://www.youtube.com/watch?v=YAfwNEY826I&t=250s I think that since i have just understood much more artificial intelligence, i will soon show you my next Open source software project that implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO, and i will write an article that explain much more artificial intelligence and what is smartness and what is consciousness and self-awareness.. Read my previous thoughts to understand: I am a white arab, and i think i am smart since i have invented many scalable algorithms.. And in only one day i have just learned "much" more artificial intelligence, i have read the following article about Particle Swarm Optimization and i have understood it: Artificial Intelligence - Particle Swarm Optimization https://docs.microsoft.com/en-us/archive/msdn-magazine/2011/august/artificial-intelligence-particle-swarm-optimization But i have just noticed that the above implementation doesn't guarantee the optimal convergence. So here is how to guarantee the optimal convergence in PSO: Clerc and Kennedy in (Trelea 2003) propose a constriction coefficient parameter selection guidelines in order to guarantee the optimal convergence, here is how to do it with PSO: v (t+1) = k*[(v(t) + (c1 * r1 * (p(t) – x(t)) + (c2 * r2 * (g(t) – x(t))] x(t+1) = x(t) + v(t+1) constriction coefficient parameter is: k = 2/abs(2-phi-sqrt(phi^2-(4*phi))) k:=2/abs((2-4.1)-(0.640)) = 0.729 phi = c1 + c2 To guarantee the optimal convergence use: c1 = c2 = 2.05 phi = 4.1 => k equal to 0.729 w=0.7298 Population size = 60; Also i have noticed that GA(genetic algorithm) doesn't guarantee the optimal convergence, and SA(Simulated annealing) and Hill Climbing are much less powerful since they perform only exploitation. In general, any metaheuristic should perform two main searching capabilities (Exploration and Exploitation). Population based algorithms ( or many solutions ) such as GA, PSO, ACO, or ABC, performs both Exploration and Exploitation, while Single-Based Algorithm such as SA(Simulated annealing), Hill Climbing, performs the exploitation only. In this case, more exploitation and less exploration increases the chances for trapping in local optima. Because the algorithm does not have the ability to search in another position far from the current best solution ( which is Exploration). Simulated annealing starts in one valley and typically ends in the lowest point of the same valley. Whereas swarms start in many different places of the mountain range and are searching for the lowest point in many valleys simultaneously. And in my next Open source software project i will implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO. Thank you, Amine Moulay Ramdane. |
| aminer68@gmail.com: Aug 11 02:30PM -0700 Hello, Read again, i correct about more smartness about what is artificial intelligence.. I am a white arab, and i think i am smart since i have invented many scalable algorithms, and when you are smart you will easily understand artificial intelligence, this is why i am finding artificial intelligence easy to learn, i think to be able to understand artificial intelligence you have to understand reasoning with energy minimization, like with PSO(Particle Swarm Optimization), but you have to be smart since the Population based algorithm has to guarantee the optimal convergence, and this is why i am learning you how to do it(read below), i think that GA(genetic algorithm) is good for teaching it, but GA(genetic algorithm) doesn't guarantee the optimal convergence, and after learning how to do reasoning with energy minimization in artificial intelligence, you have to understand what is transfer learning in artificial intelligence with PathNet or such, this transfer learning permits to train faster and require less labeled data, also PathNET is much more powerful since also it is higher level abstraction in artificial intelligence.. Read about it here: https://mattturck.com/frontierai/ And read about PathNet here: https://medium.com/@thoszymkowiak/deepmind-just-published-a-mind-blowing-paper-pathnet-f72b1ed38d46 Read my previous thoughts: More about artificial intelligence.. You have to understand me, i am going really fast in understanding artificial intelligence, since i think i am a white arab that is smart since i have invented many scalable algorithms, but i think one of the most important part in artificial intelligence is reasoning with energy minimization, it is the one that i am working on right now, see the following video to understand more about it: Yann LeCun: Can Neural Networks Reason? https://www.youtube.com/watch?v=YAfwNEY826I&t=250s I think that since i have just understood much more artificial intelligence, i will soon show you my next Open source software project that implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO, and i will write an article that explain much more artificial intelligence and what is smartness and what is consciousness and self-awareness.. Read my previous thoughts to understand: I am a white arab, and i think i am smart since i have invented many scalable algorithms.. And in only one day i have just learned "much" more artificial intelligence, i have read the following article about Particle Swarm Optimization and i have understood it: Artificial Intelligence - Particle Swarm Optimization https://docs.microsoft.com/en-us/archive/msdn-magazine/2011/august/artificial-intelligence-particle-swarm-optimization But i have just noticed that the above implementation doesn't guarantee the optimal convergence. So here is how to guarantee the optimal convergence in PSO: Clerc and Kennedy in (Trelea 2003) propose a constriction coefficient parameter selection guidelines in order to guarantee the optimal convergence, here is how to do it with PSO: v (t+1) = k*[(v(t) + (c1 * r1 * (p(t) – x(t)) + (c2 * r2 * (g(t) – x(t))] x(t+1) = x(t) + v(t+1) constriction coefficient parameter is: k = 2/abs(2-phi-sqrt(phi^2-(4*phi))) k:=2/abs((2-4.1)-(0.640)) = 0.729 phi = c1 + c2 To guarantee the optimal convergence use: c1 = c2 = 2.05 phi = 4.1 => k equal to 0.729 w=0.7298 Population size = 60; Also i have noticed that GA(genetic algorithm) doesn't guarantee the optimal convergence, and SA(Simulated annealing) and Hill Climbing are much less powerful since they perform only exploitation. In general, any metaheuristic should perform two main searching capabilities (Exploration and Exploitation). Population based algorithms ( or many solutions ) such as GA, PSO, ACO, or ABC, performs both Exploration and Exploitation, while Single-Based Algorithm such as SA(Simulated annealing), Hill Climbing, performs the exploitation only. In this case, more exploitation and less exploration increases the chances for trapping in local optima. Because the algorithm does not have the ability to search in another position far from the current best solution ( which is Exploration). Simulated annealing starts in one valley and typically ends in the lowest point of the same valley. Whereas swarms start in many different places of the mountain range and are searching for the lowest point in many valleys simultaneously. And in my next Open source software project i will implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO. Thank you, Amine Moulay Ramdane. |
| aminer68@gmail.com: Aug 11 02:20PM -0700 Hello, More smartness about what is artificial intelligence.. I am a white arab, and i think i am smart since i have invented many scalable algorithms, and when you are smart you will easily understand artificial intelligence, this is why i am finding artificial intelligence easy to learn, i think to be able to understand artificial intelligence you have to understand reasoning with energy minimization, like with PSO(Particle Swarm Optimization), but you have to be smart since the Population based algorithm has to guarantee the optimal convergence, and this is why i am learning you how to do it(read below), i think that GA(genetic algorithm) is good for teaching it, but GA(genetic algorithm) doesn't guarantee the optimal convergence, and after learning how to do reasoning with energy minimization in artificial intelligence, you have to understand what is transfer learning in artificial intelligence with PathNet or such, this transfer learning permits to train faster and require less labeled data, also PathNET is much more powerful since also it is higher level abstraction in artificial intelligence.. Read about it here: https://mattturck.com/frontierai/ And read about PathNet here: https://medium.com/@thoszymkowiak/deepmind-just-published-a-mind-blowing-paper-pathnet-f72b1ed38d46 Read my previous thoughts: More about artificial intelligence.. You have to understand me, i am going really fast in understanding artificial intelligence, since i think i am a white arab that is smart since i have invented many scalable algorithms, but i think one of the most important part in artificial intelligence is reasoning with energy minimization, it is the one that i am working on right now, see the following video to understand more about it: Yann LeCun: Can Neural Networks Reason? https://www.youtube.com/watch?v=YAfwNEY826I&t=250s I think that since i have just understood much more artificial intelligence, i will soon show you my next Open source software project that implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO, and i will write an article that explain much more artificial intelligence and what is smartness and what is consciousness and self-awareness.. Read my previous thoughts to understand: I am a white arab, and i think i am smart since i have invented many scalable algorithms.. And in only one day i have just learned "much" more artificial intelligence, i have read the following article about Particle Swarm Optimization and i have understood it: Artificial Intelligence - Particle Swarm Optimization https://docs.microsoft.com/en-us/archive/msdn-magazine/2011/august/artificial-intelligence-particle-swarm-optimization But i have just noticed that the above implementation doesn't guarantee the optimal convergence. So here is how to guarantee the optimal convergence in PSO: Clerc and Kennedy in (Trelea 2003) propose a constriction coefficient parameter selection guidelines in order to guarantee the optimal convergence, here is how to do it with PSO: v (t+1) = k*[(v(t) + (c1 * r1 * (p(t) – x(t)) + (c2 * r2 * (g(t) – x(t))] x(t+1) = x(t) + v(t+1) constriction coefficient parameter is: k = 2/abs(2-phi-sqrt(phi^2-(4*phi))) k:=2/abs((2-4.1)-(0.640)) = 0.729 phi = c1 + c2 To guarantee the optimal convergence use: c1 = c2 = 2.05 phi = 4.1 => k equal to 0.729 w=0.7298 Population size = 60; Also i have noticed that GA(genetic algorithm) doesn't guarantee the optimal convergence, and SA(Simulated annealing) and Hill Climbing are much less powerful since they perform only exploitation. In general, any metaheuristic should perform two main searching capabilities (Exploration and Exploitation). Population based algorithms ( or many solutions ) such as GA, PSO, ACO, or ABC, performs both Exploration and Exploitation, while Single-Based Algorithm such as SA(Simulated annealing), Hill Climbing, performs the exploitation only. In this case, more exploitation and less exploration increases the chances for trapping in local optima. Because the algorithm does not have the ability to search in another position far from the current best solution ( which is Exploration). Simulated annealing starts in one valley and typically ends in the lowest point of the same valley. Whereas swarms start in many different places of the mountain range and are searching for the lowest point in many valleys simultaneously. And in my next Open source software project i will implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO. Thank you, Amine Moulay Ramdane. Hello... More about artificial intelligence.. You have to understand me, i am going really fast in understanding artificial intelligence, since i think i am a white arab that is smart since i have invented many scalable algorithms, but i think one of the most important part in artificial intelligence is reasoning with energy minimization, it is the one that i am working on right now, see the following video to understand more about it: Yann LeCun: Can Neural Networks Reason? https://www.youtube.com/watch?v=YAfwNEY826I&t=250s I think that since i have just understood much more artificial intelligence, i will soon show you my next Open source software project that implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO, and i will write an article that explain much more artificial intelligence and what is smartness and what is consciousness and self-awareness.. Read my previous thoughts to understand: I am a white arab, and i think i am smart since i have invented many scalable algorithms.. And in only one day i have just learned "much" more artificial intelligence, i have read the following article about Particle Swarm Optimization and i have understood it: Artificial Intelligence - Particle Swarm Optimization https://docs.microsoft.com/en-us/archive/msdn-magazine/2011/august/artificial-intelligence-particle-swarm-optimization But i have just noticed that the above implementation doesn't guarantee the optimal convergence. So here is how to guarantee the optimal convergence in PSO: Clerc and Kennedy in (Trelea 2003) propose a constriction coefficient parameter selection guidelines in order to guarantee the optimal convergence, here is how to do it with PSO: v (t+1) = k*[(v(t) + (c1 * r1 * (p(t) – x(t)) + (c2 * r2 * (g(t) – x(t))] x(t+1) = x(t) + v(t+1) constriction coefficient parameter is: k = 2/abs(2-phi-sqrt(phi^2-(4*phi))) k:=2/abs((2-4.1)-(0.640)) = 0.729 phi = c1 + c2 To guarantee the optimal convergence use: c1 = c2 = 2.05 phi = 4.1 => k equal to 0.729 w=0.7298 Population size = 60; Also i have noticed that GA(genetic algorithm) doesn't guarantee the optimal convergence, and SA(Simulated annealing) and Hill Climbing are much less powerful since they perform only exploitation. In general, any metaheuristic should perform two main searching capabilities (Exploration and Exploitation). Population based algorithms ( or many solutions ) such as GA, PSO, ACO, or ABC, performs both Exploration and Exploitation, while Single-Based Algorithm such as SA(Simulated annealing), Hill Climbing, performs the exploitation only. In this case, more exploitation and less exploration increases the chances for trapping in local optima. Because the algorithm does not have the ability to search in another position far from the current best solution ( which is Exploration). Simulated annealing starts in one valley and typically ends in the lowest point of the same valley. Whereas swarms start in many different places of the mountain range and are searching for the lowest point in many valleys simultaneously. And in my next Open source software project i will implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO. Thank you, Amine Moulay Ramdane. |
| aminer68@gmail.com: Aug 11 12:52PM -0700 Hello.. More about artificial intelligence.. You have to understand me, i am going really fast in understanding artificial intelligence, since i think i am a white arab that is smart since i have invented many scalable algorithms, but i think one of the most important part in artificial intelligence is reasoning with energy minimization, it is the one that i am working on right now, see the following video to understand more about it: Yann LeCun: Can Neural Networks Reason? https://www.youtube.com/watch?v=YAfwNEY826I&t=250s I think that since i have just understood much more artificial intelligence, i will soon show you my next Open source software project that implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO, and i will write an article that explain much more artificial intelligence and what is smartness and what is consciousness and self-awareness.. Read my previous thoughts to understand: I am a white arab, and i think i am smart since i have invented many scalable algorithms.. And in only one day i have just learned "much" more artificial intelligence, i have read the following article about Particle Swarm Optimization and i have understood it: Artificial Intelligence - Particle Swarm Optimization https://docs.microsoft.com/en-us/archive/msdn-magazine/2011/august/artificial-intelligence-particle-swarm-optimization But i have just noticed that the above implementation doesn't guarantee the optimal convergence. So here is how to guarantee the optimal convergence in PSO: Clerc and Kennedy in (Trelea 2003) propose a constriction coefficient parameter selection guidelines in order to guarantee the optimal convergence, here is how to do it with PSO: v (t+1) = k*[(v(t) + (c1 * r1 * (p(t) – x(t)) + (c2 * r2 * (g(t) – x(t))] x(t+1) = x(t) + v(t+1) constriction coefficient parameter is: k = 2/abs(2-phi-sqrt(phi^2-(4*phi))) k:=2/abs((2-4.1)-(0.640)) = 0.729 phi = c1 + c2 To guarantee the optimal convergence use: c1 = c2 = 2.05 phi = 4.1 => k equal to 0.729 w=0.7298 Population size = 60; Also i have noticed that GA(genetic algorithm) doesn't guarantee the optimal convergence, and SA(Simulated annealing) and Hill Climbing are much less powerful since they perform only exploitation. In general, any metaheuristic should perform two main searching capabilities (Exploration and Exploitation). Population based algorithms ( or many solutions ) such as GA, PSO, ACO, or ABC, performs both Exploration and Exploitation, while Single-Based Algorithm such as SA(Simulated annealing), Hill Climbing, performs the exploitation only. In this case, more exploitation and less exploration increases the chances for trapping in local optima. Because the algorithm does not have the ability to search in another position far from the current best solution ( which is Exploration). Simulated annealing starts in one valley and typically ends in the lowest point of the same valley. Whereas swarms start in many different places of the mountain range and are searching for the lowest point in many valleys simultaneously. And in my next Open source software project i will implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO. Thank you, Amine Moulay Ramdane. |
| aminer68@gmail.com: Aug 11 12:08PM -0700 Hello, About artificial intelligence.. I am a white arab, and i think i am smart since i have invented many scalable algorithms.. And in only one day i have just learned "much" more artificial intelligence, i have read the following article about Particle Swarm Optimization and i have understood it: Artificial Intelligence - Particle Swarm Optimization https://docs.microsoft.com/en-us/archive/msdn-magazine/2011/august/artificial-intelligence-particle-swarm-optimization But i have just noticed that the above implementation doesn't guarantee the optimal convergence. So here is how to guarantee the optimal convergence in PSO: Clerc and Kennedy in (Trelea 2003) propose a constriction coefficient parameter selection guidelines in order to guarantee the optimal convergence, here is how to do it with PSO: v (t+1) = k*[(v(t) + (c1 * r1 * (p(t) – x(t)) + (c2 * r2 * (g(t) – x(t))] x(t+1) = x(t) + v(t+1) constriction coefficient parameter is: k = 2/abs(2-phi-sqrt(phi^2-(4*phi))) k:=2/abs((2-4.1)-(0.640)) = 0.729 phi = c1 + c2 To guarantee the optimal convergence use: c1 = c2 = 2.05 phi = 4.1 => k equal to 0.729 w=0.7298 Population size = 60; Also i have noticed that GA(genetic algorithm) doesn't guarantee the optimal convergence, and SA(Simulated annealing) and Hill Climbing are much less powerful since they perform only exploitation. In general, any metaheuristic should perform two main searching capabilities (Exploration and Exploitation). Population based algorithms ( or many solutions ) such as GA, PSO, ACO, or ABC, performs both Exploration and Exploitation, while Single-Based Algorithm such as SA(Simulated annealing), Hill Climbing, performs the exploitation only. In this case, more exploitation and less exploration increases the chances for trapping in local optima. Because the algorithm does not have the ability to search in another position far from the current best solution ( which is Exploration). Simulated annealing starts in one valley and typically ends in the lowest point of the same valley. Whereas swarms start in many different places of the mountain range and are searching for the lowest point in many valleys simultaneously. And in my next Open source software project i will implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO. Thank you, Amine Moulay Ramdane. |
| You received this digest because you're subscribed to updates for this group. You can change your settings on the group membership page. To unsubscribe from this group and stop receiving emails from it send an email to comp.programming.threads+unsubscribe@googlegroups.com. |
No comments:
Post a Comment