![]() It is used widely in artificial intelligence, for reaching a goal state from a starting node. The relative simplicity of the algorithm makes it a popular first choice amongst optimizing algorithms. repeated local search), or more complex schemes based on iterations (like iterated local search), or on memory (like reactive search optimization and tabu search), or on memory-less stochastic modifications (like simulated annealing). : 253 To attempt to avoid getting stuck in local optima, one could use restarts (i.e. ![]() Examples of algorithms that solve convex problems by hill-climbing include the simplex algorithm for linear programming and binary search. Hill climbing finds optimal solutions for convex problems – for other problems it will find only local optima (solutions that cannot be improved upon by any neighboring configurations), which are not necessarily the best possible solution (the global optimum) out of all possible solutions (the search space). Eventually, a much shorter route is likely to be obtained. The algorithm starts with such a solution and makes small improvements to it, such as switching the order in which two cities are visited. ![]() It is easy to find an initial solution that visits all the cities but will likely be very poor compared to the optimal solution. ![]() If the change produces a better solution, another incremental change is made to the new solution, and so on until no further improvements can be found.įor example, hill climbing can be applied to the travelling salesman problem. It is an iterative algorithm that starts with an arbitrary solution to a problem, then attempts to find a better solution by making an incremental change to the solution. In numerical analysis, hill climbing is a mathematical optimization technique which belongs to the family of local search. Hill-climbing techniques are well-suited for optimizing over such surfaces, and will converge to the global maximum. ![]()
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