and systematically as possible. See also Local search is a sub-field of: Metaheuristics Stochastic optimization Optimization Fields within local search include: Hill climbing Simulated annealing (suited for either local or global search) Tabu search Late acceptance hill climbing Reactive search optimization (combining machine learning and local search heuristics) Real-valued search-spaces Several methods exist for performing local search of real-valued search-spaces: Luus–Jaakola searches locally using a uniform distribution and an exponentially decreasing search-range. Random optimization searches locally using a normal distribution. Random search searches locally by sampling a hypersphere surrounding the current position. Pattern search takes steps along the axes of the search-space using exponentially decreasing step sizes. References Bibliography Hoos, H.H. and Stutzle, T. (2005) Stochastic Local Search: Foundations and Applications, Morgan Kaufmann. Vijay Arya and Naveen Garg and Rohit Khandekar and Adam Meyerson and Kamesh Munagala and Vinayaka Pandit, (2004): Local Search Heuristics for k-Median and Facility Location Problems, SIAM Journal of Computing 33(3). Juraj Hromkovič: Algorithmics for Hard Problems: Introduction to Combinatorial Optimization, Randomization, Approximation, and Heuristics (Springer) Wil Michiels, Emile Aarts, Jan Korst: Theoretical Aspects of Local Search (Springer) Metaheuristics Optimization algorithms and methods