Eps 7: Local Search Artificial Intelligence
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Local search algorithms are widely applied to numerous hard computational problems, including problems from computer science (particularly artificial intelligence ), mathematics , operations research , engineering , and bioinformatics .
A local search algorithm starts from a candidate solution and then iteratively moves to a neighbor solution.
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A common decision is when the best solution - to finish the search for an algorithm does not improve over a certain number of steps or when it happens.
This happens when the termination is due to the impossibility to improve the solution, because the optimal solution may lie far outside the solutions crossed by the algorithm. A local search algorithm is an approach to an incomplete algorithm, so that the search is terminated if the best solution found by an algorithm is not optimal. Local search returns a valid solution every time it is interrupted until the end. Whenever a "local search" for an algorithm takes place, it returns the valid solutions, even if they are interrupted each time until their end.
Unfortunately, pure optimization problems do not find a high-quality solution that reaches the target state from the current state, or a high-quality solution to reach the "target state" from the current state.
Note that the objective function is a function whose value is minimized or maximized in various contexts of the optimization problem. In the case of a search algorithm, it can be either the way or the cost of reaching the destination node. Define the value of this function as the number of nodes in the current state or as a high quality solution to reach the target state.
The algorithm is complete when it finds the optimum , but it is not optimal when the objective function finds a global maximum. The biggest problem with a mountain tour - the search for a climber - is that he gets stuck at the local maximum. There are two ways to find the global optimum: either you find the heuristic cost function or you find a target function. In the case of a local search algorithm, the algorithm is not optimal until it has found the international maximum, or at least the maximum of the current state.
You can optimize mountaineering by starting with a complicated and valid condition and then gradually working to improve it to an ever better condition.
Local search algorithms consume very little or a constant amount of memory because they work on a single path. As they move, they do their job by generally following the adjacent nodes and completing their tasks as quickly as possible. Unlike a classic search algorithm, we have a "local search algorithm" whose path costs focus only on the state of solution required to reach the destination node.
In large, infinite states, where the classical systematic algorithms do not work, a reasonable solution can be found in a very short time.
Practical algorithms for solving SAT can be divided into two main types: a complete algorithm that develops from the DPLL method, and a stochastic local search algorithm based on the GSAT algorithm. While heuristic search is the most common method for solving computationally intensive combination problems, S LS can also be considered a kind of heuristic search.
SLS algorithms are able to solve a number of specific types of SAT instances very quickly, especially random satisfactory instances, but they are usually incomplete and cannot prove dissatisfaction. Although it is important to solve industrial problems encrypted by an SAT instance, the ability of SLS to solve SAT itself is of great importance both in theory and practice.
Theoretically, the random 3-SAT problem is an important test of the ability of SLS algorithms to solve industrial problems. In practice, it can be a useful tool for dealing with a variety of problems such as computer vision, machine learning and data analysis. The random 3 SAT instance also provides a relatively unbiased and unruly test set for evaluating practical algorithms, making it an ideal test case for developing new algorithms for industrial applications.
It is important to note that this multistart method can be implemented in several ways, such as through a local search algorithm. We now perform the local search algorithm for the random 3-SAT problem, starting with various randomly generated solutions and the global termination criteria. Now we analyze and present the solution values achieved by the presented heuristic algorithm.
We are conducting the experiment with the same parameters used in previous research on this subject, such as the local search algorithm, global termination criteria, and the random 3-SAT problem.
The algorithm is similar to UCS, except that we use the A algorithm and combine the greedy best - the first search and the local search algorithm to solve the problem efficiently. Using a heuristic function, the search algorithms find the shortest way through a search space.