Existential Risk

Evolutionary Algorithms are optimization techniques inspired by natural selection and genetics. They generate a population of candidate solutions, then iteratively select the best performers to produce new solutions through operations like mutation and crossover. Over successive generations, the algorithms improve solutions, making them effective for complex problems where traditional methods struggle. They are widely used in machine learning for optimization tasks.