Mutation

Definition of Mutation

Mutation: A mutation in data science is an alteration of the data, which can be intentional or accidental.

How is Mutation used?

Mutation is a term that is used when talking about machine learning algorithms, particularly genetic algorithms. It refers to a process of randomly making changes or modifications to the data that has been inputted into the system, which can be used to help the algorithm more effectively find solutions to the problem it is trying to solve. In most cases, mutation involves adding small changes or perturbations to the existing data in order to generate new values that may be better suited for exploration and adaptation.

In genetic algorithms, mutation typically takes place during each generation of evolutionary search. As part of this process, a random selection of individuals in a population will be chosen and then modified according to some specific rules or conditions. These modifications are designed to introduce some variation into the gene pool within each population, so as to increase the likelihood of finding an optimal solution.

The type of mutation used in a particular genetic algorithm will depend on what type of problem is being addressed. For example, if the goal is to optimize a design parameter (such as length) then one might use bit-flipping mutations; otherwise if the goal is to maximize an objective function then one might use random walk mutations or simulated annealing mutations depending on the parameters of interest. Regardless of what kind of mutation is chosen, it plays an important role in helping search algorithms explore new regions and discover potentially better solutions than those found previously.