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Weekly Report on Genetic Algorithms and Neural Networks Research Progress

This report outlines the work completed by Bang-Xuan Huang during the week of February 15, 2012. Key achievements include a survey of genetic algorithms applied within DLM, focusing on three primary steps: reproduction (utilizing fitness functions such as roulette wheel selection and tournament selection), crossover (single, double, and average), and mutation. Additionally, the report summarizes insights gained from Chapter 7 of R. Rojas's book on Neural Networks, touching upon proposed methods for input-output relevance and feed-forward back-propagation strategies. Future work will emphasize contextual information incorporation and weight updates in neural network training.

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Weekly Report on Genetic Algorithms and Neural Networks Research Progress

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  1. 2012.02.15Weekly report Bang-Xuan Huang

  2. What I have done this week • Survey • Genetic algorithm used in DLM • Genetic algorithm three steps 1.reproduction(fitness function) -roulette wheel selection (maybe we can use in MDLM) -tournament selection 2.crossover(single, double, average) 3.mutation

  3. What I have done this week • Read • Chapter 7 by R. Rojas: Neural Networks, Springer-Verlag, Berlin, 1996

  4. Proposed method input hidden output Previous state delayed class

  5. Past method input hidden output Relevance Previous state class delayed

  6. Feed-forward Back-propagation Sen3 b1 b2 b3 Sen1 w1 w2 w3 w4 w5 Sen2 a1 a2 Proposed method • Add relevance information w1 w3 w2 ….. ….. 1 2 1 0 … 1 2 1 0 … 1 2 1 0 … 01 0 0 … 0 0 1 0 … 1 0 0 0 … Future work • Add context information

  7. Back-propagation algorithm Feed-forward computation Back-propagation to the output layer Back-propagation to the hidden layer Weight updates

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