Genetic Algorithms

Genetic Algorithms are a type of optimization algorithm inspired by the process of natural selection. They are used to find the optimal solution to a problem by mimicking the process of evolution. The algorithm starts with a population of potential solutions and applies genetic operators such as selection, crossover, and mutation to create new generations of solutions. The fitness of each solution is evaluated and the best solutions are selected to create the next generation. This process continues until a satisfactory solution is found. Genetic Algorithms are commonly used in optimization problems where the search space is large and complex, and traditional optimization methods are not effective.

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Search Space: In the context of data science and artificial intelligence, search space refers to the set of all possible solutions that can be generated by an algorithm to solve a given problem. It is the range of all possible combinations of parameters ... Read More >>
E-learning: E-learning is the delivery of education and training through digital resources such as computers, tablets, and smartphones. It involves the use of multimedia materials, interactive activities, and online assessments to facilitate learning. ... Read More >>
Random Forest: Random Forest is a machine learning algorithm that is used for classification, regression and other tasks. It is an ensemble learning method that combines multiple decision trees to create a forest. Each decision tree is built using a rando ... Read More >>

Genetic Algorithms

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