solving algorithm

Project: Design a decision fusion tool to perform weighted fusion. The weights are adjusted using hill climbing algorithm. Introducing random walk/jump/restart or trying simulated annealing to improve performance is optional. Menu Specification (minimum): Train & Decide Train (this option will compute decision weights) Input: the following data (read data from a text file containing only Y/N) Decision 1 Decision 2 ………… Decision n-1 Decision n Actual Decision Example 1 Y N ………… Y Y Y Example 2 Y Y ………… Y N Y …………….. …………….. …………….. ………… …………….. …………….. …………….. Example m-1 N N ………… N N N Example m N N ………… Y Y N Output: W1, W2, …Wn-1, Wn where Wi is the decision weigh for decision i. Algorithm: 1. Initialization: Randomly assign weights between 0 and 1 for each decision 2. Use the following formula to compute decision: a. If ( WY >  WN) the decision is Y else if ( WY <  WN) the decision is N, where WY is the summation of all decision weights for the decision “Y”. b. Use the following formula to compute accuracy: Accuracy= v/m, where v number of decisions (computed in step 2a) that match with the actual decision 3. For i= 1 to n Keep change (try increasing and decreasing) decision weight in small steps for decision i as long as accuracy improves 4. Repeat step 3 as long as accuracy improves 5. Output the finals weights W1, W2, …Wn-1, Wn (and store them) Decide (This option will perform decision fusion and output the decision) Input: Decision 1, Decision 2, … Decision n Output: Y or N Algorithm: If ( WY >  WN) the decision is Y else if ( WY <  WN) the decision is N.