Brute Force vs Exhaustive Search: Difference and Comparison

In computer science, many searching algorithms have been developed by different developers to search distinct aspects (strings, numbers, patterns, solutions).

Searching is carried out in a variety of ways. Brute force and Exhaustion search are two string searching algorithms used by the coders. These methods work on the principle of searching for every feasible solution.

Key Takeaways

1. Brute force attacks systematically test every possible combination to crack passwords or encryption.
2. Exhaustive search algorithms evaluate all potential solutions to find the optimal one.
3. Both methods can be resource-intensive but effectively solve problems without specific heuristics.

Brute Force vs Exhaustive Search

Brute force is a non-uniform search that uses a trial and error to guess login info, encryption keys, or find a hidden web page. Exhaustive search is a type of brute force search used to solve problems related to permutation and combination. It is a uniform search and takes less time.

The brute force algorithm is a technique coded by the developers to fetch strings, and even it is used in solving the eight-queen puzzle (a puzzle to place eight queens on eight by eight chessboard).

However, it’s an intuitive strategy that necessitates a lot of comparisons to solve the problem.

The exhaustive search is a type of brutal force search that is used to solve problems related to permutation and combination.

The main aim is to search for every solution for the optimal solution by satisfying the constraints. Other problems can also be sorted, such as traveling salesman and knapsack problems.

Comparison Table

Parameters of Comparison	Brute Force Search	Exhaustive Search
Type of Search	Brute Force is a non-uniform search.	Exhaustive is a uniform search because we are aware in which direction fetching is done.
Searches	Brute Force is for searching the string pattern.	The exhaustive search algorithm is for fetching permutation, combination, and subsets.
Procedure	The Brute Force algorithm searches for the desired pattern by moving towards the right in a given text.	The exhaustive search algorithm examines each node until it reaches the final node.
Time	The Brute Force is a time-consuming method.	The exhaustive search algorithm takes less time in comparison.
Applications	The Brute Force search algorithm is used in placing eight queens on eight-by-eight boards.	The exhaustive search algorithm is used for solving the traveling salesman problem.

What is Brute Force Search?

The Brute Force algorithm is one of the searching techniques in computer science. Since it is an intuitive method, it is a very straightforward approach for solving problems that are based solely on predictions.

In this method, no complex technique is used for finding the solution.

The process is to keep iterating through the text for searching the string. If a string mismatches, move one step to the right and repeat the process until the appropriate match is found.

The procedure is now simple. Suppose we have to search the string PLANT.

Match the spelling of each word in the paragraph with the string PLANT. Move rightwards if a phrase in the line does not match. If a string matches, our search has been successful. We have received the desired results.

Therefore, we can say this is a very time-consuming search method if the length of the text is longer. The trick of calculating the number of comparisons is the multiplication of N x M where N is the length of the text and M is the length of the string.

For Example,

Text =10

String= PLANT so, size of string =5

Combinations = N X M = 10 x 5= 50

In practical life, we can use brute force search. An example is placing eight queens on 8×8 chess boards. The rule is to arrange queens in such a degree that no one queen blocks the route of another.

What is Exhaustive Search?

The exhaustive search is a subset of the Brute Force search algorithm, which is for searching combinations and permutations. This algorithm focuses on finding each solution to the given probleam by satisfying all the constraints.

Because exhaustive means tiring, these types of searches are blind searches, but uniform ones. The strategy aims at either maximizing or minimizing the problem.

Many problems can be resolved using an exhaustive search, such as the traveling salesman problem and knapsack problem.

The traveling salesman’s problem is that before returning to the starting place, the salesman must visit the N cities (only once) by using the shortest route.

Here N is the number of the cities, and the constraints under this problem are:

1. Following the shortest path from city to city.
2. Visit all the cities before coming back.
3. Visit all the cities only once.

For Example,

There are five cities: A, B, C, D, and E. The starting city is chosen wisely by applying the combinations. So, that all the constraints are fulfilled.

While choosing an appropriate path, we will be trying several combinations that will be exhausting and time-consuming. In other words, we have to form a cyclic movement to achieve the target.

Main Differences Between Brute Force and Exhaustive Search

1. The Brute Force search algorithm is the non-uniform method. On the other hand, an exhaustive search algorithm is a uniform method.
2. The Brute Force search algorithm is a method for searching the string in the text. On the contrary, an exhaustive search algorithm searches the solution of permutations and combinations.
3. The Brute Force technique works by matching all of the letters in a string. However, an exhaustive search follows the procedure of examing every node of the flowchart until the constraints are satisfied.
4. The Brute Force method is more timing-consuming and is applicable when data is short. On the other side, an exhaustive algorithm is applicable even in complex scenarios.
5. Since the Brute Force method is followed in the exhaustive search technique, it is more popular than an exhaustive search.

References

1. https://ieeexplore.ieee.org/abstract/document/4640789/
2. https://link.springer.com/chapter/10.1007/3-540-44411-4_2