This algorithm uses an approach which is quite similar to the best first search algorithm. It is a simple best first search which reduces the estimated cost of reach the goal. Basically it takes the closest node that appears to be closest to the goal. This search starts with the initial matrix and makes very single possible changes then looks at the change it made to the score. This search then applies the change till the greatest improvement. The search continues until no further improvement can be made. The greedy search never makes never makes a lateral move .It uses minimal estimated cost h (n) to the goal state as measure which decreases the search time but the algorithm is neither complete nor optimal. The main advantage of this search is that it is simple and finds solution quickly. The disadvantages are that it is not optimal, susceptible to false start.
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