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In numerical analysis, hill climbing is a mathematical optimization technique which belongs to the family of local search. 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.
Sub areas of the map tend to hold their colors stable and min conflicts cannot hill climb to break out of the local minimum. The CONFLICTS function counts the number of constraints violated by a particular object, given that the state of the rest of the assignment is known.
The most basic form of local search is based on choosing the change that maximally decreases the cost of the solution. This method, called hill climbing, proceeds as follows: first, a random assignment is chosen; then, a value is changed so as to maximally improve the quality of the resulting assignment. If no solution has been found after a ...
When the choice of the neighbor solution is done by taking the one locally maximizing the criterion, i.e.: a greedy search, the metaheuristic takes the name hill climbing. When no improving neighbors are present, local search is stuck at a locally optimal point.
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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.
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