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Introduction
Many search algorithm revolve around the internet and some yet to be published so far the sit as a draft in the Researcher's PC. This search methodology doesn't directly going to command the algorithm to move left, right, top, bottom. It just rank the moves based on the previous search experience and the lessons learnt from previous and current searches in order to effectively and efficiently travel and search the remaining part of the tree.
Requirements
Neural-Net is going to collect information about both the node and path.
Yes! you heard it right it collects data about path also.Information that are collected by Neural-Net:
- Information belonging to the Path
- Rank ( which depends on the success rate* )
- Redirections in path ( For real-time Usage**)
- Information belongs to Node
- Value
- Maximum, Minimum, and Average Branch Factor.
- Number of Child levels.
- Nearness to solution.
*Success Rate: From the previous experience the Neural-Net calculate the success rate(i.e., How many time the solution is found while traversing through this path). The Path is ranked based on the success rate.
** Some Nodes may be temporarily or permanently down there redirection will be provided.
For example: If a particular road is under construction the re-direction will be provided so the Google Map has to provide a re-direction to particular node. It also works in Network of computers and routers.
** Some Nodes may be temporarily or permanently down there redirection will be provided.
For example: If a particular road is under construction the re-direction will be provided so the Google Map has to provide a re-direction to particular node. It also works in Network of computers and routers.
Working
(Now neural-net don't have to command the algorithm since the algorithm don't have any confusion because it has to enter only through root node)
- Visit the root node.
- Now confusion arises to the algorithm(which path I have to take) Then it approaches the Neural-net which suggest the algorithm with list paths along with its rank.
- The algorithm select the path with highest rank and it proceed through the path.
- (wait a minute) what happens if two or more path having similar rank. Here the Neural-net uses the information belonging to the node and then subtract a floating-point value from the path's Rank.
- Again the path will be sorted based on the Rank and the algorithm choose the path with Best value for Rank.
- If a solution is found intimate the Neural-net and terminate.
- Else repeat from the Step 2.
You may ask what happens if the path with Best Rank don't lead to solution which in turn leads to a wasted search time. The neural-net collect information about node that is the nearness of solution so the algorithm doesn't search all the search space belong to the best path.
Conclusion
It is quite obvious that this pedagogy may look like Heuristic-Search Technique. But it is quite novel it in its own way that it uses Neural-net to backpropagate the results of the search to get a better search experience than ordinary Heuristic-Search algorithms.
For more articles Subscribe and follow my blog and me. If you have doubts in expanding and exploring research with this article kindly send me mail( balakawshik2000@gmail.com ) regarding your doubt. I am open to clarify.
Attribution:
Images were taken from unsplash.com
Under a free license scheme. Images were used with proper attribution in the ALT tag of every Image.
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