Genetic Algorithms
Genetic algorithms are a class of optimization and search algorithms inspired by the principles of natural selection and genetics, used to find approximate solutions to complex problems. They work by evolving a population of candidate solutions over generations through selection, crossover (recombination), and mutation operations. These algorithms are particularly effective for problems where traditional methods are inefficient, such as NP-hard problems, scheduling, or parameter tuning.
Developers should learn genetic algorithms when tackling optimization problems with large search spaces, non-linear constraints, or where gradient-based methods fail, such as in machine learning hyperparameter tuning, robotics path planning, or financial portfolio optimization. They are valuable in fields like artificial intelligence, engineering design, and bioinformatics, offering a robust approach to explore solutions without requiring derivative information or explicit problem structure.