Adaptive Algorithm

An adaptive algorithm is a type of computer program that can automatically improve its performance over time. Unlike traditional algorithms, which are designed to solve specific problems, adaptive algorithms are designed to be general-purpose. This means that they can be applied to a wide range of tasks, and they will automatically adjust their behavior as new data is encountered. One important property of adaptive algorithms is that they can learn from their mistakes. If an algorithm makes a mistake, it can adjust its future behavior to avoid making the same mistake again. This type of learning is known as online learning, and it is a key feature of many successful adaptive algorithms. Another important property of adaptive algorithms is that they can scale up to handle very large datasets. This is because they do not need to store all of the data in memory. Instead, they can process it in small pieces and gradually improve their performance. As a result, adaptive algorithms are well suited for solving problems that are too large for traditional algorithms to handle.