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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.
Beam search with width 3 (animation) In computer science, beam search is a heuristic search algorithm that explores a graph by expanding the most promising node in a limited set. Beam search is a modification of best-first search that reduces its memory requirements. Best-first search is a graph search which orders all partial solutions (states ...
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 ...
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.
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."
Pages for logged out editors learn more. Contributions; Talk; Hill climbing algorithm
An intuitive explanation of the algorithm from "Numerical Recipes": [5] The downhill simplex method now takes a series of steps, most steps just moving the point of the simplex where the function is largest (“highest point”) through the opposite face of the simplex to a lower point.
Simulated annealing searching for a maximum. The objective here is to get to the highest point. In this example, it is not enough to use a simple hill climb algorithm, as there are many local maxima. By cooling the temperature slowly the global maximum is found.