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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

    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. 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. 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).

  6. Stochastic hill climbing - Wikipedia

    en.wikipedia.org/wiki/Stochastic_hill_climbing

    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."

  7. Snake catcher delighted by python’s ‘incredible’ climbing ...

    www.aol.com/finance/snake-catcher-delighted...

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  8. Powell's method - Wikipedia

    en.wikipedia.org/wiki/Powell's_method

    The method is useful for calculating the local minimum of a continuous but complex function, especially one without an underlying mathematical definition, because it is not necessary to take derivatives. The basic algorithm is simple; the complexity is in the linear searches along the search vectors, which can be achieved via Brent's method.

  9. 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 ...