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How do algorithms adapt to solve dynamic real-world problems?

Algorithms adapt to solve dynamic real-world problems through learning and optimisation techniques, often using artificial intelligence (AI) and machine learning (ML).

Algorithms are essentially a set of rules or instructions that a computer follows to solve a problem or complete a task. In static problems, these rules are fixed and do not change. However, real-world problems are often dynamic, meaning they change over time or depending on the context. To handle this dynamism, algorithms need to be able to adapt and learn from new data or situations.

One way algorithms adapt is through machine learning, a subset of AI. Machine learning algorithms use statistical techniques to learn from data. They can improve their performance as they are exposed to more data over time. For example, a machine learning algorithm used for email spam filtering learns to identify spam by being trained on a large dataset of spam and non-spam emails. As it processes more emails, it gets better at distinguishing spam from non-spam.

Another way algorithms adapt is through optimisation techniques. These are methods used to adjust the parameters of an algorithm to improve its performance. For example, a route-finding algorithm might initially suggest a route based on the shortest distance. However, it could adapt to consider other factors like traffic conditions or road closures, optimising the route to find the quickest rather than the shortest path.

Algorithms can also adapt through reinforcement learning, another subset of machine learning. In reinforcement learning, an algorithm learns to make decisions by interacting with its environment and receiving feedback. For instance, a reinforcement learning algorithm used in a game-playing AI learns to make better moves by playing the game repeatedly and receiving rewards or penalties based on its performance.

In summary, algorithms adapt to solve dynamic real-world problems by learning from data, optimising their performance, and interacting with their environment. These techniques allow algorithms to improve over time and handle the complexity and unpredictability of real-world situations.

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