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  2. Local search (optimization) - Wikipedia

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

    Local search is an anytime algorithm; it can return a valid solution even if it's interrupted at any time after finding the first valid solution. Local search is typically an approximation or incomplete algorithm because the search may stop even if the current best solution found is not optimal. This can happen even if termination happens ...

  3. Local search (constraint satisfaction) - Wikipedia

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

    The aim of local search is that of finding an assignment of minimal cost, which is a solution if any exists. Point A is not a solution, but no local move from there decreases cost. However, a solution exists at point B. Two classes of local search algorithms exist. The first one is that of greedy or non-randomized algorithms. These algorithms ...

  4. Guided local search - Wikipedia

    en.wikipedia.org/wiki/Guided_local_search

    Guided local search is a metaheuristic search method. A meta-heuristic method is a method that sits on top of a local search algorithm to change its behavior. Guided local search builds up penalties during a search. It uses penalties to help local search algorithms escape from local minima and plateaus.

  5. WalkSAT - Wikipedia

    en.wikipedia.org/wiki/WalkSAT

    In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI'93), pages 290–295. Bart Selman, Henry Kautz, and Bram Cohen. "Local Search Strategies for Satisfiability Testing." Final version appears in Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, October 11–13, 1993.

  6. Beam search - Wikipedia

    en.wikipedia.org/wiki/Beam_search

    Since local beam search often ends up on local maxima, a common solution is to choose the next states in a random way, with a probability dependent from the heuristic evaluation of the states. This kind of search is called stochastic beam search. [12] Other variants are flexible beam search and recovery beam search. [11]

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  8. Artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence

    There are two very different kinds of search used in AI: state space search and local search. State space search For example, planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis .

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