Genetic Algorithm
A genetic algorithm is a metaheuristic optimization technique inspired by the process of natural selection and genetics. It uses mechanisms such as selection, crossover, and mutation to evolve a population of candidate solutions toward an optimal or near-optimal solution for a given problem. It is commonly applied to complex search, optimization, and machine learning tasks where traditional methods are inefficient.
Developers should learn genetic algorithms when tackling optimization problems with large, complex search spaces, such as scheduling, routing, parameter tuning, or feature selection in machine learning. They are particularly useful for non-linear, multi-modal, or NP-hard problems where gradient-based methods fail or are impractical, offering a robust approach to finding good solutions without requiring derivatives or explicit problem structure.