Identify a common perceptual, neurological, or cognitive issue and discuss contributing factors. Outline steps for prevention or health promotion for the patient and family.
can be generalized that such an algorithm can be effective in environments without much computing power, especially when the environment is ‘bumpy’ 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 ‘model’ of a search space’s 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 backpropagation’s 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 solu>GET ANSWER