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Stochastic hill climbing is a variant of the basic hill climbing method. While basic hill climbing always chooses the steepest uphill move, "stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphill move." [1]
Steepest ascent hill climbing is similar to best-first search, which tries all possible extensions of the current path instead of only one. [2] Stochastic hill climbing does not examine all neighbors before deciding how to move. Rather, it selects a neighbor at random, and decides (based on the amount of improvement in that neighbor) whether to ...
Simultaneous perturbation stochastic approximation; Social cognitive optimization; Space allocation problem; Space mapping; Special ordered set; Spiral optimization algorithm; Stochastic dynamic programming; Stochastic gradient Langevin dynamics; Stochastic hill climbing; Stochastic programming; Subgradient method; Successive linear programming
Iterated Local Search [1] [2] (ILS) is a term in applied mathematics and computer science defining a modification of local search or hill climbing methods for solving discrete optimization problems. Local search methods can get stuck in a local minimum, where no improving neighbors are available.
Hill climbing algorithms can only escape a plateau by doing changes that do not change the quality of the assignment. As a result, they can be stuck in a plateau where the quality of assignment has a local maxima. GSAT (greedy sat) was the first local search algorithm for satisfiability, and is a form of hill climbing.
Conversely, a beam width of 1 corresponds to a hill-climbing algorithm. [3] The beam width bounds the memory required to perform the search. Since a goal state could potentially be pruned, beam search sacrifices completeness (the guarantee that an algorithm will terminate with a solution, if one exists).
Gradient algorithms that add a momentum term to each dimension tend to perform even better than Stochastic Gradient Ascent. I'm actually implementing a stochastic hill climbing algorithm and I have no earthly idea what the heck this page is on about. This page could use an informal, intuitive description of the hill climbing concept.
Sub areas of the map tend to hold their colors stable and min conflicts cannot hill climb to break out of the local minimum. The CONFLICTS function counts the number of constraints violated by a particular object, given that the state of the rest of the assignment is known.