# Tag Info

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As @nbro has already said that Hill Climbing is a family of local search algorithms. So, when you said Hill Climbing in the question I have assumed you are talking about the standard hill climbing. The standard version of hill climb has some limitations and often gets stuck in the following scenario: Local Maxima: Hill-climbing algorithm reaching on the ...

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Hill climbing is not an algorithm, but a family of "local search" algorithms. Specific algorithms which fall into the category of "hill climbing" algorithms are 2-opt, 3-opt, 2.5-opt, 4-opt, or, in general, any N-opt. See chapter 3 of the paper "The Traveling Salesman Problem: A Case Study in Local Optimization" (by David S. Johnson and Lyle A. McGeoch) for ...

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When we climb a hill: We move higher in altitude. The person who is climbing, will always look for rocks/mud on the hill that are higher, so that he can climb higher. That is what the algorithm does too. We are assuming that there is a hill of numbers. The larger numbers are placed higher than the smaller numbers. So if we want to climb up the hill, we ...

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Let's begin with some definitions first. Hill-climbing is a search algorithm simply runs a loop and continuously moves in the direction of increasing value-that is, uphill. The loop terminates when it reaches a peak and no neighbour has a higher value. Stochastic hill climbing, a variant of hill-climbing, chooses a random from among the uphill moves. The ...

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The steepest hill climbing algorithms works well for convex optimization. However, real world problems are typically of the non-convex optimization type: there are multiple peaks. In such cases, when this algorithm starts at a random solution, the likelihood of it reaching one of the local peaks, instead of the global peak, is high. Improvements like ...

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Tabu search uses memory to rule out parts of the neighborhood for local search, allowing the trajectory to typically pass through local optima instead of getting stuck in them.

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You could parallelize the search by dividing the global space in distinct regions/subsets. Then apply in each region a local search. This way you can search the global space systematically, more exhaustively and perhaps in different ways (e.g by applying a different local search method to each region). Finally you can compare the results and choose the best ...

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First of all you need an initial solution. You will then improve this solution with hill climbing. For your initial solution, you can color the map randomly using the K colors. This will most likely result in conflicts (adjacent regions of the same color). Then the hill climbing part: Find a region which has conflicts and swap its color for another color, ...

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Let's see their definition first: Best First Search (BFS): ‌ Best-first search is a search algorithm that explores a graph by expanding the most promising node chosen according to a specified rule. estimating the promise of node n by a "heuristic evaluation function ${\displaystyle f(n)}$ which, in general, may depend on the description of n, the ...

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This part of your sentence is not always true "and not reached to final goal/solution". If you have just one maximum at all and it is finite, hill climbing (HL) can reach to it and it is a global maximum too (for example, if the function is a parabola). To answer your question is back to the stop criteria of HL. It will be stopped when reaching to a maximum ...

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In the least technical, most intuitive way possible: Simulated Annealing can be considered as a modification of Hill Climbing (or Hill Descent). Hill Climbing/Descent attempts to reach an optimum value by checking if its current state has the best cost/score in its neighborhood, this makes it prone to getting stuck in local optima. Simulated Annealing ...

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First we have to specify the problem: Initial State: The map all colored randomly. Successor Function (Transition Model): Change the color of a region. Goal Test: The map all colored such that two adjacent regions do not share a color. Cost Function: Assigns 1 to change the color of a region. Now that we have the specification of the problem, we have to ...

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This question really looks like a homework problem, in part because it is too vague (what does it mean to 'get stuck' exactly?). Hill climbing stops when it reaches a local maximum. Hill climbing is an uninformed search algorithm, so it does not make use of a heuristic. Hill climbing may or may not stop on a ridge, depending on the implementation. Some ...

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Very interesting paper, I did not know you could get such results using traditional image processing. Question 1 From the paper: Since only average feature vector values of $R_1$ and $R_2$ need to be found, we use the integral image approach as used in [14] for computational efficiency. A change in scale is affected by scaling the region $R_2$ instead of ...

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No, they are prone to get stuck in local maxima, unless the whole search space is investigated. A simple algorithm will only ever move upwards; if you imagine you're in a mountain range, this will not get you very far, as you will need to go down before going up higher. You can see that going down a bit will have a net benefit, but the search algorithm will ...

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Best-first search BFS is a search approach and not just a single algorithm, so there are many best-first (BFS) algorithms, such as greedy BFS, A* and B*. BFS algorithms are informed search algorithms, as opposed to uninformed search algorithms (such as breadth-first search, depth-first search, etc.), i.e. BFS algorithms make use of domain knowledge that can ...

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I will give you a basic idea of an approach. The basic idea behind hill climbing algorithms is to find local neighbouring solutions to the current one and, eventually, replace the current one with one of these neighbouring solutions. So, you first need to model your problem in a way such that you can find neighbouring solutions to the current solution (as ...

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In general, hill climbing algorithms select a random initial solution, then takes the best move available after evaluating all possible operations available. The possible operations are determined by the search operators (which you determine and is dependent on the problem setting). So by your words, you should 'replace it with the fittest solution among its ...

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There is no reason why you can't have a hill descending algorithm, instead of finding maxima you will find minima. If that is what your aim is, it's still called a hill climbing algorithm, I guess...

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I'm new to these concepts too, but the way I've understood it, Stochastic hill climbing would perform better in cases where computation time is precious (includes the calculation of the fitness function) but it is not really necessary to reach the best possible solution. Reaching even a local optimum would be ok. Robots operating in a swarm would be one ...

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I think there are at least three points that you need to think before implement Hill-Climbing (HC) algorithm: First, the initial state. In HC, people usually use a "temporary solution" for the initial state. You can use an empty knapscak, but I prefer to randomly pick items and put it in the knapsack as the initial state. You can use a binary array to save ...

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