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  2. Hill climbing - Wikipedia

    en.wikipedia.org/wiki/Hill_climbing

    Random-restart hill climbing is a meta-algorithm built on top of the hill climbing algorithm. It is also known as Shotgun hill climbing . It iteratively does hill-climbing, each time with a random initial condition x 0 {\displaystyle x_{0}} .

  3. Min-conflicts algorithm - Wikipedia

    en.wikipedia.org/wiki/Min-conflicts_algorithm

    One such algorithm is min-conflicts hill-climbing. [1] Given an initial assignment of values to all the variables of a constraint satisfaction problem (with one or more constraints not satisfied), select a variable from the set of variables with conflicts violating one or more of its constraints.

  4. 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.

  5. 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.

  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

    When applicable, a common approach is to iteratively improve a parameter guess by local hill-climbing in the objective function landscape. Derivative-based algorithms use derivative information of to find a good search direction, since for example the gradient gives the direction of steepest ascent. Derivative-based optimization is efficient at ...

  8. Hill climbing algorithm - Wikipedia

    en.wikipedia.org/?title=Hill_climbing_algorithm&...

    Pages for logged out editors learn more. Contributions; Talk; Hill climbing algorithm

  9. Talk:Hill climbing - Wikipedia

    en.wikipedia.org/wiki/Talk:Hill_climbing

    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. It would be useful for many people who are new to optimization. Eternallyoptimistic 14:19, 26 April 2007 (UTC)