Evolutionary Algorithms
Evolutionary Algorithms are a family of optimization and search techniques inspired by biological evolution, including natural selection, mutation, and crossover. They are used to solve complex problems by iteratively improving a population of candidate solutions over generations. These algorithms are particularly effective for problems where traditional methods struggle, such as those with large search spaces or non-linear constraints.
Developers should learn Evolutionary Algorithms when tackling optimization problems in fields like machine learning, robotics, or game development, where solutions need to adapt to dynamic environments. They are useful for parameter tuning, feature selection, and designing complex systems, as they can handle multi-objective and noisy optimization scenarios efficiently.