Nature, a source of minerals and precious stones is a mine of algorithms too. In nature there are phenomena that resemble sorting action, phenomena which closely resemble division operation and so on. By observing and studying these phenomena computer algorithms can be extracted.

It is possible to identify or observe natural phenomena from which algorithms can be derived, by accident or through a random search. At the same time, it is difficult to propose a formal procedure (a set of well-defined steps) to search for a natural system that solves the problem at hand. Nevertheless, the following suggestions given in the form of an algorithm will be useful in hunting for a suitable natural system.

1. Try to associate the data items that are central to the problem with entities in nature. Let their attributes (mass, length, volume etc.) represent the magnitude of the data items.

2. Any algorithm transforms the input data, which is in a particular state into another form, which is the output data. Therefore, keeping the entities in mind, try imagining a particular state of the system that depicts the input data and another state that depicts the output.

3. Now try to conceive a set of activities that can change the system state from the input state to the output state.

The above steps augmented with a little intuition should help the problem solver to zero-in onto a suitable natural system.

Uniform-cost search (UCS) is a tree search algorithm used for traversing or searching a weighted tree, tree structure, or graph. The search begins at the root node. The search continues by visiting the next node, which has the least total cost from the root. Nodes are visited in this manner until a goal state is reached.

Typically, the search algorithm involves expanding nodes by adding all unexpanded neighboring nodes that are connected by directed paths to a priority queue. In the queue, each node is associated with its total path cost from the root, where the least-cost paths are given highest priority. The node at the head of the queue is subsequently expanded, adding the next set of connected nodes with the total path cost from the root to the respective node.

BFS is an uninformed search method that aims to expand and examine all nodes of a graph or combination of sequences by systematically searching through every solution. In other words, it exhaustively searches the entire graph or sequence without considering the goal until it finds it. It does not use a heuristic algorithm.

From the standpoint of the algorithm, all child nodes obtained by expanding a node are added to a FIFO (i.e., First In, First Out) queue. In typical implementations, nodes that have not yet been examined for their neighbors are placed in some container (such as a queue or linked list) called “open” and then once examined are placed in the container “closed”.

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