Hill Climbing
Hill climbing is a local search optimization algorithm used in computer science and artificial intelligence to find a good solution to optimization problems. It starts with an arbitrary solution and iteratively makes small changes to improve it, moving 'uphill' toward a local optimum. However, it can get stuck in local maxima and may not find the global optimum.
Developers should learn hill climbing for solving optimization problems where finding an exact solution is computationally expensive, such as scheduling, routing, or parameter tuning in machine learning. It's particularly useful when a quick, approximate solution is acceptable, and the problem space is too large for exhaustive search, but it requires careful design to avoid local optima pitfalls.