An exhaustive search that looks across all the given inputs and does not limit itself to clustering or approximations. It’s usually more expensive and time-consuming, but it thoroughly completes the job. In artificial intelligence (AI), brute force search is used to find a solution to a problem by repeatedly trying each possible way until one works. Although this method can be helpful for solving modest problems, the exponential number of probable solutions renders it impractical for those with more complex requirements.
Applications of Brute Force Search
Despite the widespread adoption of more sophisticated search algorithms, brute force search is still employed in some subfields of the artificial intelligence (AI) field. Some examples are as follows:
1. Gaming
Programmers use brute-force search algorithms to find the best strategy in games like chess and Go. For instance, Google DeepMind’s AlphaZero algorithm plays these games superhumanly by combining Monte Carlo Tree Search with deep neural networks.
2. Cryptography
Brute-force search can be used in cryptography to decipher encrypted messages and crack codes. For instance, a brute force search can be used to decrypt a message encrypted with a simple substitution cipher to try every possible letter substitution.
3. Cracking passwords
A Brute Force Search can try every possible combination of characters to break a password.
4. Grid searches
Grid search is a method for hyperparameter tuning in machine learning in which every possible combination of hyperparameters is explored to locate the optimal settings. This method may be laborious and time-consuming, but it can yield good results for constrained hyperparameter spaces.
5. Optimization
Finding the shortest route between two points is an example of an optimization problem that can benefit from thoroughly searching all possible solutions using a brute-force search algorithm. This method, however, can work only with relatively modest issues.
6. Image recognition
Template matching is a method that employs a search through all possible orientations and sizes of the template image to locate the best match with the target image. Some images can be recognized with an extensive database of known images and a brute-force search algorithm. However, this strategy typically provides lower accuracy and efficiency compared to current machine learning methods, such as convolutional neural networks.
7. Natural language processing
In natural language processing, brute force search can be used to sift through a large corpus of potential sentences in search of the one that most closely matches the given input.
8. Planning & scheduling
Brute force search can generate plans and schedules by iteratively searching through all possible sequences of actions and picking the one that satisfies the desired criteria. Heuristic search and other more effective algorithms have primarily supplanted this method because they can be applied to more extensive problems more efficiently.
9. Data mining
Using brute force search, data miners can sift through many potential patterns in the data to locate the most relevant and insightful ones. In association rule mining, for instance, brute force search can be used to explore all possible permutations of data to identify the most common pairings. However, faster algorithms like Apriori often replace this method because its inefficient neural networks are trained with brute force search.
