genetic algorithm
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AI overview
A genetic algorithm is a search method that works like evolution—it starts with many possible solutions, tests which ones work best, and then combines and modifies the winning solutions to find even better answers. This approach matters because it can tackle complex problems where traditional methods get stuck, making it useful for everything from engineering design to scheduling tasks.
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The 2006 NASA ST5 spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. It is known as an evolved antenna.
A genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA) in computer science and operations research. Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems via biologically inspired operators such as selection, crossover, and mutation. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, and causal inference.