Search algorithm 28249 217922738 2008-06-08T09:38:36Z 77.49.13.195 {{Unreferenced|date=August 2007}} In [[computer science]], a '''search algorithm''', broadly speaking, is an [[algorithm]] that takes a problem as [[input]] and returns a solution to the problem, usually after evaluating a number of possible solutions. Most of the algorithms studied by computer scientists that solve problems are kinds of search algorithms. The set of all possible solutions to a problem is called the [[search space]]. [[Brute-force search]] or "naïve"/uninformed search algorithms use the simplest, most intuitive method of searching through the search space, whereas informed search algorithms use [[heuristic function]]s to apply knowledge about the structure of the [[search space]] to try to reduce the amount of time spent searching. == Uninformed search == An uninformed search algorithm is one that does not take into account the specific nature of the problem. As such, they can be implemented in general, and then the same [[implementation]] can be used in a wide range of problems thanks to [[Abstraction (computer science)|abstraction]]. The drawback is that most [[search space]]s are extremely large, and an uninformed search (especially of a tree) will take a reasonable amount of time only for small examples. As such, to speed up the process, sometimes only an informed search will do. == List search == List search algorithms are perhaps the most basic kind of search algorithm. The goal is to find one element of a set by some key (perhaps containing other information related to the key). As this is a common problem in [[computer science]], the [[computational complexity]] of these algorithms has been well studied. The simplest such algorithm is [[linear search]], which simply examines each element of the list in order. It has expensive [[big O notation|O]](n) running time, where ''n'' is the number of items in the list, but can be used directly on any unprocessed list. A more sophisticated list search algorithm is [[binary search]]; it runs in [[big O notation|O]](log ''n'') time. This is significantly better than [[linear search]] for large lists of data, but it requires that the list be sorted before searching (see [[sorting algorithm]]) and also be [[random access]]. [[Interpolation search]] is better than binary search for large sorted lists with fairly even distributions, but has a worst-case running time of O(''n''). [[Grover's algorithm]] is a [[quantum computer|quantum algorithm]] that offers quadratic speedup over the classical linear search for unsorted lists. However, it requires a currently non-existent quantum computer on which to run. [[Hash table]]s are also used for list search, requiring only [[constant time]] for search in the average case, but more space overhead and terrible O(''n'') worst-case search time. Another search based on specialized data structures uses [[self-balancing binary search tree]]s and requires O(log ''n'') time to search; these can be seen as extending the main ideas of binary search to allow fast insertion and removal. See [[associative array]] for more discussion of list search data structures. Most list search algorithms, such as linear search, binary search, and self-balancing binary search trees, can be extended with little additional cost to find all values less than or greater than a given key, an operation called ''range search''. The glaring exception is hash tables, which cannot perform such a search efficiently. == Tree search == [[Tree search algorithm]]s are the heart of searching techniques. These search [[tree (graph theory)|tree]]s of [[node (computer science)|node]]s, whether that tree is explicit or implicit (generated on the go). The basic principle is that a [[node (computer science)|node]] is taken from a [[data structure]], its successors examined and added to the data structure. By manipulating the data structure, the tree is explored in different orders for instance level by level ([[breadth-first search]]) or reaching a [[leaf node]] first and backtracking ([[depth-first search]]). Other examples of tree-searches include [[iterative deepening depth-first search|iterative-deepening search]], [[depth-limited search]], [[bidirectional search]], and [[uniform-cost search]]. ==Graph search== Many of the problems in [[graph theory]] can be solved using [[graph traversal]] algorithms, such as [[Dijkstra's algorithm]], [[Kruskal's algorithm]], the [[nearest neighbour algorithm]], and [[Prim's algorithm]]. These can be seen as extensions of the tree-search algorithms. == Informed search == In an informed search, a [[heuristic (function)|heuristic]] that is specific to the problem is used as a guide. A good heuristic will make an informed search dramatically out-perform any uninformed search. There are few prominent informed list-search algorithms. A possible member of that category is a hash table with a hashing function that is a heuristic based on the problem at hand. Most informed search algorithms explore trees. These include [[best-first search]], and [[A Star Search Algorithm|A*]]. Like the uninformed algorithms, they can be extended to work for graphs as well. == Adversarial search == In games such as [[chess]], there is a [[game tree]] of all possible moves by both players and the resulting board configurations, and we can search this tree to find an effective playing strategy. This type of problem has the unique characteristic that we must account for any possible move our opponent might make. To account for this, game-playing computer programs, as well as other forms of [[artificial intelligence]] like [[machine planning]], often use search algorithms like the [[minimax algorithm]], [[search tree pruning]], and [[alpha-beta pruning]]. == Constraint satisfaction == This is a type of search which solves [[constraint satisfaction problem]]s where, rather than looking for a path, the solution is simply a set of values assigned to a set of variables. Because the variables can be processed in any order, the usual tree search algorithms are too inefficient. Methods of solving constraint problems include [[combinatorial search]] and [[backtracking]], both of which take advantage of the freedom associated with constraint problems. Common tricks or techniques involved in [[backtracking]] is Constraint propagation, which is a general form of Forward checking. Other local search algorithms, such as generic algorithm, which minimize the conflicts, also do a good job. == Other types == * [[String searching algorithm]]s search for patterns within [[string (computer science)|strings]]; one popular data structure that makes this more efficient is the [[suffix tree]] * [[Genetic algorithms]] and [[Genetic programming]] use ideas from [[evolution]] as heuristics for reducing the search space * [[Sorting algorithm]]s necessary for executing certain search algorithms * [[Simulated annealing]] is a [[probabilistic]] search algorithm * [[Recommender system]]s also use statistical methods to rank results in very large data sets * [[Tabu search]] is a technique to avoid discrete searches getting stuck in local minima * [[Federated search]] * [[Minimax]] which can be highly optimized using [[alpha-beta pruning]] is an algorithm to search for good moves in [[zero-sum]] games * [[Ternary search]] ==See also== * [[Selection algorithm]] * [[No free lunch in search and optimization]] * [[Secretary problem]] is an online (ie sequentially presented) search problem with imperfect information, and a statistically optimal strategy. ==External links== *[http://www.gp-field-guide.org.uk/ A Field Guide to Genetic Programming] by Poli, Langdon, and McPhee. Available as a free PDF, or in printed form from Lulu.com. * [http://en.wikiversity.org/wiki/Uninformed_Search_Project Self-Guided Lesson on Uninformed Search] Go to the Wikiversity and teach yourself to program an uninformed search solution. [[Category:Search algorithms| ]] [[de:Suchverfahren]] [[el:Επίλυση προβλημάτων]] [[es:Algoritmo de búsqueda]] [[fr:Algorithme de recherche]] [[id:Algoritma pencarian]] [[it:Algoritmo di ricerca]] [[nl:Zoekalgoritme]] [[ja:探索]] [[pt:Algoritmo de busca]] [[fi:Hakualgoritmi]] [[vi:Giải thuật tìm kiếm]]