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  2. Hill climbing - Wikipedia

    en.wikipedia.org/wiki/Hill_climbing

    Hill climbing attempts to maximize (or minimize) a target function (), where is a vector of continuous and/or discrete values. At each iteration, hill climbing will adjust a single element in and determine whether the change improves the value of ().

  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...

    A drawback of hill climbing with moves that do not decrease cost is that it may cycle over assignments of the same cost. Tabu search [1] [2] [3] overcomes this problem by maintaining a list of "forbidden" assignments, called the tabu list. In particular, the tabu list typically contains only the most recent changes.

  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. Local search (optimization) - Wikipedia

    en.wikipedia.org/wiki/Local_search_(optimization)

    In computer science, local search is a heuristic method for solving computationally hard optimization problems. Local search can be used on problems that can be formulated as finding a solution that maximizes a criterion among a number of candidate solutions.

  7. 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).

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  9. Graduated optimization - Wikipedia

    en.wikipedia.org/wiki/Graduated_optimization

    An illustration of graduated optimization. Graduated optimization is an improvement to hill climbing that enables a hill climber to avoid settling into local optima. [4] It breaks a difficult optimization problem into a sequence of optimization problems, such that the first problem in the sequence is convex (or nearly convex), the solution to each problem gives a good starting point to the ...