![\"\"](\"https:\/\/prepbytes-misc-images.s3.ap-south-1.amazonaws.com\/assets\/1676638092592-How%20do%20you%20Solve%20the%20N%20Queen%20Problem.jpg\")
<\/p>\n<\/p>\n
The backtracking algorithm is used to solve the N queen problem, in which the board is recursively filled with queens, one at a time, and backtracked if no valid solution is found. At each step, the algorithm checks to see if the newly placed queen conflicts with any previously placed queens, and if so, it backtracks. The process is repeated until all N queens are placed on the board without conflict, or until all possible solutions are exhausted. The resulting board is a correct solution to the N queen problem.<\/p>\n
Backtracking is a problem-solving technique that involves recursively trying out different solutions to a problem, and backtracking or undoing previous choices when they don’t lead to a valid solution. It is commonly used in algorithms that search for all possible solutions to a problem, such as the famous eight-queens puzzle. Backtracking is a powerful and versatile technique that can be used to solve a wide range of problems.<\/p>\n
Backtracking is often used in constraint satisfaction problems, such as the N Queen problem, where we need to find a solution that satisfies a set of constraints. In these problems, we start by choosing a value for one of the variables and checking if it satisfies the constraints. If it does, we move on to the next variable and repeat the process. If the current value does not satisfy the constraints, we backtrack to the previous variable and try a different value.<\/p>\n
Backtracking is an effective technique for solving problems, as it allows us to incrementally build a solution and avoids the need to consider all possible solutions from scratch. However, it can also be time-consuming, as it requires many iterations and checks. To make backtracking more efficient, various optimization techniques, such as constraint propagation and heuristics, can be used.<\/p>\n
The N-Queens Problem is a chess puzzle in which N chess queens must be placed on a NxN chessboard so that no two queens threaten each other. It has received extensive research in computer science and mathematics, and it is frequently used as a standard for evaluating algorithms and heuristics. The problem has a long history and practical applications in scheduling, data encryption, and pattern recognition.<\/p>\n
For example, the two possible solutions to the 4 Queen problem are shown below:<\/p>\n
![\"\"](\"https:\/\/prepbytes-misc-images.s3.ap-south-1.amazonaws.com\/assets\/1676638092695-How%20do%20you%20Solve%20the%20N%20Queen%20Problem1.png\")
<\/p>\n
The N Queen problem can be solved by using various approaches, such as brute force, backtracking, and genetic algorithms. Backtracking is one of the popular approaches for solving the N Queen problem, and it is simple to implement and can be made more efficient with optimization techniques.<\/p>\n
The complete algorithm for the N queen problem is discussed below : <\/p>\n
Below we have discussed approaches to solve the N Queen Problem<\/p>\n
In this approach, we generate all possible queen configurations on board and print a configuration that satisfies the given constraints.<\/p>\n
Below is the pseudo-code for Naive Approach<\/strong><\/p>\n By using backtracking we position queens in different columns one by one, beginning with the leftmost column. When we position a queen in a column, we look for clashes with other queens that have already been placed. If we locate a row in the current column with no collision, we mark this row and column as part of the solution. We backtrack and return false if we cannot discover such a row due to clashes.<\/p>\n Algorithm:<\/strong>while there are unexplored configurations.\n{\n generate the next configuration\n if queens don't attack in this configuration then\n {\n print this configuration;\n }\n}<\/code><\/pre>\nApproach 2: Solving N Queen Problem using backtracking<\/h3>\n
\nHere’s an algorithm to solve the n queen problem:<\/p>\n\n