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The particel swarm: Explosion, stability, and convergence in a multidimensional complex space
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTION
"... The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained ..."
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Cited by 852 (10 self)
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The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately
Depth first search and linear graph algorithms
 SIAM JOURNAL ON COMPUTING
, 1972
"... The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components of an undirect ..."
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Cited by 1406 (19 self)
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The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components
Genetic Algorithm with Automatic Termination and Search Space Rotation
, 2016
"... and search space rotation ..."
Depthfirst IterativeDeepening: An Optimal Admissible Tree Search
 Artificial Intelligence
, 1985
"... The complexities of various search algorithms are considered in terms of time, space, and cost of solution path. It is known that breadthfirst search requires too much space and depthfirst search can use too much time and doesn't always find a cheapest path. A depthfirst iteratiwdeepening a ..."
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Cited by 527 (24 self)
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The complexities of various search algorithms are considered in terms of time, space, and cost of solution path. It is known that breadthfirst search requires too much space and depthfirst search can use too much time and doesn't always find a cheapest path. A depthfirst iteratiw
Evolutionary Exploration of Search Spaces
 Foundations of Intelligent Systems, number 1079 in Lecture Notes in Computer Science
, 1996
"... . Exploration and exploitation are the two cornerstones of problem solving by search. Evolutionary Algorithms (EAs) are search algorithms that explore the search space by the genetic search operators, while exploitation is done by selection. During the history of EAs different operators have emerged ..."
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Cited by 2 (0 self)
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. Exploration and exploitation are the two cornerstones of problem solving by search. Evolutionary Algorithms (EAs) are search algorithms that explore the search space by the genetic search operators, while exploitation is done by selection. During the history of EAs different operators have
Suffix arrays: A new method for online string searches
, 1991
"... A new and conceptually simple data structure, called a suffix array, for online string searches is introduced in this paper. Constructing and querying suffix arrays is reduced to a sort and search paradigm that employs novel algorithms. The main advantage of suffix arrays over suffix trees is that ..."
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Cited by 835 (0 self)
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is that, in practice, they use three to five times less space. From a complexity standpoint, suffix arrays permit online string searches of the type, "Is W a substring of A?" to be answered in time O(P + log N), where P is the length of W and N is the length of A, which is competitive with (and
Rtrees: A Dynamic Index Structure for Spatial Searching
 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA
, 1984
"... In order to handle spatial data efficiently, as required in computer aided design and geodata applications, a database system needs an index mechanism that will help it retrieve data items quickly according to their spatial locations However, traditional indexing methods are not well suited to data ..."
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Cited by 2750 (0 self)
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to data objects of nonzero size located m multidimensional spaces In this paper we describe a dynamic index structure called an Rtree which meets this need, and give algorithms for searching and updating it. We present the results of a series of tests which indicate that the structure performs well
An Optimal Algorithm for Approximate Nearest Neighbor Searching in Fixed Dimensions
 ACMSIAM SYMPOSIUM ON DISCRETE ALGORITHMS
, 1994
"... Consider a set S of n data points in real ddimensional space, R d , where distances are measured using any Minkowski metric. In nearest neighbor searching we preprocess S into a data structure, so that given any query point q 2 R d , the closest point of S to q can be reported quickly. Given any po ..."
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Cited by 984 (32 self)
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Consider a set S of n data points in real ddimensional space, R d , where distances are measured using any Minkowski metric. In nearest neighbor searching we preprocess S into a data structure, so that given any query point q 2 R d , the closest point of S to q can be reported quickly. Given any
Combining Search Space Diagnostics and
"... Abstractâ€”Stochastic optimisers such as Evolutionary Algorithms outperform random search due to their ability to exploit gradients in the search landscape, formed by the algorithmâ€™s search operators in combination with the objective function. Research into the suitability of algorithmic approaches to ..."
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searchbased algorithm which provides knowledge about the search space while it searches for the global optimum of a problem. It is a contribution to a less researched area which may be named Diagnostic Optimisation. I.
Search Space Extraction
"... Abstract. Systematic tree search is often used in conjunction with inference and restarts when solving challenging Constraint Satisfaction Problems (CSPs). In order to improve the efficiency of constraint solving, techniques that learn during search, such as constraint weighting and nogood learning, ..."
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Cited by 1 (0 self)
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. In this paper we propose a third way of learning during search, generalising previous work by Freuder and Hubbe. Specifically, we show how, in a restart context, we can guarantee that we avoid revisiting a previously visited region of the search space by extracting it from the problem. Likewise, we can avoid
Results 11  20
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