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Adaptive Algorithms are those algorithms that can adapt themselves according to the data/environment they are handling. This is a complicated topic and students to prepare assignments on this topic need to have a thorough understanding of the topic. This is why they approach BookMyEssay for Adaptive Algorithms assignment help.

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What are Adaptive Algorithms?

Adaptive Algorithms are those algorithms that change their behavior when they run based on the available information. This information can be received data on computational resources, the story of received data or run-time received information related to an environment where it operates. Adaptive Algorithms change their behavior when they sense they need to do that.


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For instance, in a search engine, an engine crawls an entire internet and return with results from various locations, however, it can consider known parameters regarding the user. If the IP address of the user is in France then an adaptive algorithm will return with French results or it may direct the user to a French site.

Differences Between Adaptive and Non-Adaptive Algorithms

Some of the differences between adaptive and non-adaptive Algorithms are stated in our online help with assignment on Adaptive Algorithm. Adaptive Algorithms takes benefits of useful properties of an input. Non-Adaptive Algorithms do not take such advantage. A simple instance is multiplication methods. Non-Adaptive Algorithms mean you have to multiply every digit. Adaptive Algorithm takes advantage of shifting left to multiply by 10.

In sorting, non-Adaptive Algorithms take the same time for a specified value but an adaptive algorithm takes the properties of data. It means taking advantage of an already sorted or a nearly sorted portion. Timsort is an adaptive algorithm that is used widely. Quicksort and heapsort are non-adaptive. Non-Adaptive algorithms are simple, Adaptive on the other hand, is faster but more complex.

Kinds of Adaptive Algorithms

The two common Adaptive Algorithms are called LMS or Least Mean Squares and RLS or Recursive Least Squares. Other algorithms are a variation of two of them.

LMS or The least mean square is a kind of filter that is used in machine learning and it uses gradient descent. Professionals say it an adaptive filter, which helps in signal processing in different ways. LMS uses a technique known as the method of steepest descent. It estimates results constantly by updating of filter weights. It offers learning curves helpful in machine learning implementation and theory. May ideas are a component of dedicated work on making test and training processes effective, matching inputs with outputs, and pursuing a convergence where an iterative learning process solves a final result rather than getting off track.

Recursive least squares or RLS is an adaptive algorithm that finds the coefficients recursively and it minimizes a linear cost function that is related to an input signal. This approach is different from algorithms including LMS or least mean squares that tries to minimize the mean square error. In RLS, input signals are deterministic but LMS and other algorithms are said to be stochastic. In comparison to its competitors, RLS shows a fast convergence. However, you can get this advantage at a very high cost of computational complexity.


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Applications of Adaptive Algorithms

Some applications of Adaptive Algorithms are mentioned in our Adaptive Algorithms homework help online as follows:

  • Noise Cancellation and Adaptive Echo
  • Aircraft controllers
  • Machine tools
  • Industrial applications

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