Evolutionary algorithms are optimization techniques inspired by the process of natural selection. They work by simulating the way species evolve over time, using mechanisms such as selection, mutation, and crossover. These algorithms start with a population of potential solutions and iteratively improve them by selecting the best candidates and combining their features to create new solutions. These algorithms are widely used in various fields, including artificial intelligence, engineering, and economics. They are particularly effective for solving complex problems where traditional methods may struggle, as they can explore a vast solution space and adapt to changing conditions over time.