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We can work on The Green New Deal
Environmental science is a study of current events. As new technologies develop, as politics change, and as the human population grows, our impact and influence on the environmental also changes.
For this assignment, you are to find a recent (less than 5 years old) articles addressing some aspect of environmental science that we have covered or will cover this year.
For this assignment you will write me no more than a three-page summary of the articles you found over your topic. You should use at least three (3) references for paper. Your summary should be 12 font, Times New Roman, double spaced. You need to focus on the 5 Ws and How. What are the 5 Wâs and How? ⢠Who – Who/what is the event happening to or who is exhibiting the event? ⢠What – What is the event? ⢠When – When did the event happen or when is it going to happen? ⢠Where – Where did the event happen? ⢠Why – Why did the event happen? (What is the cause of/reason for the event) ⢠How – How did it happen? or how is it possible or how are people reacting?
Sample Solution
onclusion In this paper, we have looked at four unique randomized optimization algorithms, and compared their performance on entirely different problems. To summarize, we will compare where each of our algorithms exceled, and further, what the solution of our problems tells us about the overall application of randomized optimization. Optimization Algorithms Randomized Hill Climbing comes with two distinct advantages; the algorithm itself is incredibly simple, and further, given enough iterations, a properly-implemented algorithm will eventually find a global optima. It can be generalized that such an algorithm can be effective in environments without much computing power, especially when the environment is âbumpyâ with many local optima. Simulated Annealing is an impressively fast optimization algorithm with an effective randomization technique which is capable of rapidly probing a search space. The algorithm shines on relatively simple problems (like Flip Flop), especially when finding the true global minima is not the ultimate concern. Genetic Algorithms add a novel biological approach to randomized optimization. When faced with relatively unknown, non-complex search spaces, Genetic Algorithms can locate fit minima quickly. Finally, the MIMIC algorithm adds a whole new level of complexity to randomized optimization which allows for the building of a âmodelâ of a search spaceâs probability distribution to make informed neighbor choices. With domain knowledge that a data contains such structure (and enough computing power), MIMIC can prove to be a very powerful tool. Optimization Problems The application of Genetic Algorithms, Random Hill Climbing and Simulated Annealing to the calculation of neural network weights, while inciting interesting analysis, ultimately proves that backpropagation should be the sole trainer for these models; none of the algorithms were able to come close to backpropagationâs impressive results from Assignment 1. However, our discussion of further optimization problems shows important uses of these algorithms; the Flip Flop problem proves the effectiveness of Simulated Annealing in almost instantaneously locating optima in large, simple search spaces. Furthermore, the successful application of our advanced MIMIC and Genetic Algorithm to challenging NP-complete problems show that these algorithms are crucial to developing accurate approximations when true solutions do not exist.>
onclusion In this paper, we have looked at four unique randomized optimization algorithms, and compared their performance on entirely different problems. To summarize, we will compare where each of our algorithms exceled, and further, what the solution of our problems tells us about the overall application of randomized optimization. Optimization Algorithms Randomized Hill Climbing comes with two distinct advantages; the algorithm itself is incredibly simple, and further, given enough iterations, a properly-implemented algorithm will eventually find a global optima. It can be generalized that such an algorithm can be effective in environments without much computing power, especially when the environment is âbumpyâ with many local optima. Simulated Annealing is an impressively fast optimization algorithm with an effective randomization technique which is capable of rapidly probing a search space. The algorithm shines on relatively simple problems (like Flip Flop), especially when finding the true global minima is not the ultimate concern. Genetic Algorithms add a novel biological approach to randomized optimization. When faced with relatively unknown, non-complex search spaces, Genetic Algorithms can locate fit minima quickly. Finally, the MIMIC algorithm adds a whole new level of complexity to randomized optimization which allows for the building of a âmodelâ of a search spaceâs probability distribution to make informed neighbor choices. With domain knowledge that a data contains such structure (and enough computing power), MIMIC can prove to be a very powerful tool. Optimization Problems The application of Genetic Algorithms, Random Hill Climbing and Simulated Annealing to the calculation of neural network weights, while inciting interesting analysis, ultimately proves that backpropagation should be the sole trainer for these models; none of the algorithms were able to come close to backpropagationâs impressive results from Assignment 1. However, our discussion of further optimization problems shows important uses of these algorithms; the Flip Flop problem proves the effectiveness of Simulated Annealing in almost instantaneously locating optima in large, simple search spaces. Furthermore, the successful application of our advanced MIMIC and Genetic Algorithm to challenging NP-complete problems show that these algorithms are crucial to developing accurate approximations when true solutions do not exist.>
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