Genetic Algorithms

Genetic algorithms are optimization methods inspired by natural selection. They start with a diverse group of solutions, called "populations," and evaluate how well each one performs. The best solutions are then combined and slightly altered to create new solutions, mimicking reproduction with mutation. Over successive generations, the algorithm "evolves" better solutions by selecting and refining the top performers. This process helps find optimal or near-optimal solutions to complex problems efficiently, especially ones where traditional methods struggle. Essentially, genetic algorithms use evolution principles—selection, crossover, and mutation—to intelligently explore possible solutions.