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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.

Introduction The Parallel Line Search Algorithm uses tow parallel lines to navigate to thee nodes that need to be explored. It is optimised variation of Breadth First Search. Many of search algorithm exist but they either work biased like searching left, right, top or bottom. Working Step 1: The algorithm first visit the Root node. Step 2: Then it visits both Left and Right side (unbiased). At first you may think that it works like Bidirectional Search Algorithm  Just wait a minute. Step 3: Increase the Depth and move to next level of the tree. Step 4: Find all Children. Step 5: Visit all Children. Visiting Left Child Visiting Right Child Step 6: The search continues in the same way(i.e., repeat from Step 3 until the solution is found or the entire tree). Step 7: Stop It drastically reduce the amount of required for exploration of nodes.  If branching factor of tree is b and distance of goal node from source is d, then the normal BFS/DFS searching complexity would be O(b^d)....