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  2. Hill climbing - Wikipedia

    en.wikipedia.org/wiki/Hill_climbing

    It iteratively does hill-climbing, each time with a random initial condition . The best is kept: if a new run of hill climbing produces a better than the stored state, it replaces the stored state. Random-restart hill climbing is a surprisingly effective algorithm in many cases.

  3. Min-conflicts algorithm - Wikipedia

    en.wikipedia.org/wiki/Min-conflicts_algorithm

    In fact, Constraint Satisfaction Problems that respond best to a min-conflicts solution do well where a greedy algorithm almost solves the problem. Map coloring problems do poorly with Greedy Algorithm as well as Min-Conflicts. Sub areas of the map tend to hold their colors stable and min conflicts cannot hill climb to break out of the local ...

  4. Local search (constraint satisfaction) - Wikipedia

    en.wikipedia.org/wiki/Local_search_(constraint...

    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.

  5. Iterated local search - Wikipedia

    en.wikipedia.org/wiki/Iterated_local_search

    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.

  6. Beam search - Wikipedia

    en.wikipedia.org/wiki/Beam_search

    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).

  7. Derivative-free optimization - Wikipedia

    en.wikipedia.org/wiki/Derivative-free_optimization

    For example, f might be non-smooth, or time-consuming to evaluate, or in some way noisy, so that methods that rely on derivatives or approximate them via finite differences are of little use. The problem to find optimal points in such situations is referred to as derivative-free optimization, algorithms that do not use derivatives or finite ...

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  9. Mean shift - Wikipedia

    en.wikipedia.org/wiki/Mean_shift

    Mean-shift is a hill climbing algorithm which involves shifting this kernel iteratively to a higher density region until convergence. Every shift is defined by a mean shift vector. The mean shift vector always points toward the direction of the maximum increase in the density.