Showing papers on "Best-first search published in 1991"

On the convergence of the multidirectional search algorithm

Abstract: This paper presents the convergence analysis for the multidirectional search algorithm, a direct search method for unconstrained minimization. The analysis follows the classic lines of proofs of convergence for gradient-related methods. The novelty of the argument lies in the fact that explicit calculation of the gradient is unnecessary, although it is assumed that the function is continuously differentiable over some subset of the domain. The proof can be extended to treat most nonsmooth cases of interest; the argument breaks down only at points where the derivative exists but is not continuous. Finally, it is shown how a general convergence theory can be developed for an entire class of direct search methods—which includes such methods as the factorial design algorithm and the pattern search algorithm—that share a key feature of the multidirectional search algorithm.

A tree-trellis based fast search for finding the N-best sentence hypotheses in continuous speech recognition

Abstract: A novel tree-trellis based fast search for finding the N-best sentence hypotheses in continuous speech recognition is presented. The search consists of a forward time-synchronous trellis search and a backward time-asynchronous tree search. The Viterbi algorithm is used for recording the scores of all partial paths in a trellis time synchronously. Then a backward A* algorithm based tree search is used to extend partial paths time asynchronously. Extended partial paths in the backward tree search are rank ordered in a stack by their corresponding best possible scores of the remaining paths which are prerecorded in the forward trellis path map. In each path growing cycle, the current best partial path, which is at the top of the stack, is extended by the best possible one arc (word) extension. The tree-trellis search is different from the traditional time synchronous Viterbi search in its ability to find not just the best but the N best paths of different word content. >

Delta Coding: An Iterative Search Strategy for Genetic Algorithms.

Abstract: A new search strategy for genetic algorithms is introduced which allows iterative searches with complete reinitialization of the population preserving the progress already made toward solving an optimization task. Delta coding is a simple search strategy based on the idea that the encoding used by a genetic algorithm can express a distance away from some previous partial solution. Delta values are added to a partial solution before evaluating the tness; the delta encoding forms a new hypercube of equal or smaller size that is constructed around the most recent partial solution. Results are presented on two optimization problems involving geometric transformations; solving these problems with precision is diicult for conventional genetic algorithms as well as traditional mathematical optimization techniques. Tests using single population and distributed genetic algorithms are compared to delta coding. Delta coding is shown to produce more precise solutions while reducing the amount of work necessary to reach the solution.

Moving target search

Abstract: We consider the case of heuristic search where the location of the goal may change during the course of the search. For example, the goal may be a target that is actively avoiding the problem solver. We present a moving target search algorithm (MTS) to solve this problem. We prove that if the average speed of the target is slower than that of the problem solver, then the problem solver is guaranteed to eventually reach the target. An implementation with randomly positioned obstacles confirms that the MTS algorithm is highly effective in various situations.

A search heuristic for just-in-time scheduling in parallel machines

Abstract: In recent years the Just-in-Time (JIT) production philosophy as been adopted by many companies around the world. This has motivated the study of scheduling models that embrace the essential components of JIT systems. In this paper, we present a search heurustic for the weighted earliness penalty problem with deadlines in parallel identical machines. Our approach combines elements of the solution methods known as greedy randomized adaptive search procedure (GRASP) and tabu search. It also uses a branch-and-bound post-processor to optimize individually the sequence of the jobs assigned to each machine.

Depth-first vs best-first search

Abstract: We present a comparison of three well known heuristic search algorithms: best-first search (BFS), iterative-deepening (ID), and depth-first branch-and-bound (DFBB). We develop a model to analyze the time and space complexity of these three algorithms in terms of the heuristic branching factor and solution density. Our analysis identifies the types of problems on which each of the search algorithms performs better than the other two. These analytical results are validated through experiments on different problems. We also present a new algorithm, DFS*, which is a hybrid of iterative deepening and depth-first branch-and-bound, and show that it outperforms the other three algorithms on some problems.

3,000,000 Queens in less than one minute

Abstract: The n - queens problem is a classical combinatorial search problem. In this paper we give a linear time algorithm for this problem. The algorithm is an extension of one of our previous local search algorithms [3, 4, 6]. On an IBM RS 6000 computer, this algorithm is capable of solving problems with 3,000,000 queens in approximately 55 seconds.

Global search algorithms for minimum concave-cost network flow problems

Abstract: We present algorithms for the single-source uncapacitated version of the minimum concave cost network flow problem. Each algorithm exploits the fact that an extreme feasible solution corresponds to a sub-tree of the original network. A global search heuristic based on random extreme feasible initial solutions and local search is developed. The algorithm is used to evaluate the complexity of the randomly generated test problems. An exact global search algorithm is developed, based on enumerative search of rooted subtrees. This exact technique is extended to bound the search based on cost properties and linear underestimation. The technique is accelerated by exploiting the network structure.

Parallel Algorithms for Depth-First Search

Abstract: In this paper we examine parallel algorithms for performing a depth-first search (DFS) of a directed or undirected graph in sub-linear time. this subject is interesting in part because DFS seemed at first to be an inherently sequential process, and for a long time many researchers believed that no such algorithms existed. We survey three seminal papers on the subject. The first one proves that a special case of DFS is (in all likelihood) inherently sequential; the second shows that DFS for planar undirected graphs is in NC; and the third shows that DFS for general undirected graphs is in RNC. We also discuss randomnized algorithms, Pcompleteness and matching, three topics that are essential for understanding and appreciating the results in these papers. Disciplines Theory and Algorithms Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-91-71. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/428 Parallel Algorithms For Depth-First Search MS-CIS-91-71

Optimization in real time

Abstract: A search algorithm called real-time search (RTS) for solving combinatorial optimization problems under real-time constraints is presented. The algorithm aims at finding the best possible solution within a given deadline. Since this objective is generally not achievable without first solving the problem, the authors use an alternative heuristic objective that looks for the solution with the best ascertained approximation degree. The algorithm schedules a sequence of guided depth-first searches, each searching for a more accurate solution (based on the approximation degree set), or solutions deeper in the search tree (based on the threshold set), or a combination of both. Five versions of the RTS algorithm for setting approximation degrees and/or thresholds are formulated, evaluated, and analyzed. The authors describe the experimental results obtained for the five versions of RTS. >

Useful AI tools-a review of heuristic search methods

Abstract: The classic search methodologies used in artificial intelligence are illustrated. The methods covered are depth-first, breadth-first, best-first or heuristic search, and the uniform-cost method. The power and rationale of heuristic search is discussed. >

An algorithm-structured neural net for the shortest-path problem

Abstract: A neural-net-inspired non-von-Neumann architecture which implements Dijkstra's (1959) dynamic programming algorithm to find the shortest path between two given nodes in a graph is presented. The net consists of two layers of binary higher-order neurons, where each layer is fully connected. The first layer neurons act as a recurrent net whose dynamics branches through the search tree. To guide the search, this layer is supervised by additional units that handle real-valued data involved in the search. During the search, the first layer feeds the second layer, which acts as an optimal policy table. This layer is supervised to record relevant information about the intermediate stages of the search. After the search has terminated, the first layer is no longer used. The second layer, working recurrently, outputs the optimal sequence of nodes. >

An efficient tabu search algorithm for graph bisectioning

Abstract: A new algorithm for solving the graph bisectioning problem based on tabu search is proposed. The authors run the tabu search algorithm and the Kernighan-Lin algorithm on the same set of random graphs with 50 to 500 nodes and compare their performances. They demonstrate that for all of the graphs their tabu search algorithm provides lower bisection cost than the Kernighan-Lin algorithm; and for all of the graphs with more than 200 nodes, their tabu search algorithm takes less time than the Kernighan-Lin algorithm. >

A heuristic search algorithm for on-line system identification

Abstract: The application of heuristic search techniques to system identification is described. The search algorithm is used for the development of ARMAX models of physical systems, and, in addition to heuristics used to search for appropriate polynomial orders, also contains heuristics for sampling rate selection, delay estimation, and model validation. When coded in rulebase form and coupled with an expert-supervised control architecture, a fully automated approach to modeling and control can be achieved. The development of the heuristic system identification algorithm is detailed, and a demonstration of the algorithm in the development of an ARMAX model of a sampled analog system is provided. >

A framework for the evaluation and selection of assembly plans

Abstract: The authors introduce four criteria for evaluation of assembly plans Quantitative measures corresponding to the criteria are also introduced The quantitative measures are used to search for an optimal assembly plan from the modified AND/OR graph representation of assembly plans of a product using a branch-and-bound algorithm The graph-search technique is used to avoid complete enumeration and evaluation of all feasible assembly plans of a product and to improve the efficiency of the selection process for the best plan In the search process of an optimal assembly plan, the structure of an assembly system and assembly tasks are determined >

An efficient simulated annealing algorithm for graph bisectioning

Abstract: A new simulated annealing algorithm for solving the graph bisectioning problem is proposed. The authors run their simulated annealing algorithm, the Kernighan-Lin algorithm, and the Saab-Rao algorithms on the same set of random graphs with 50 to 500 nodes and compare their performances. Experiments show that their simulated annealing algorithm provides lower bisection cost than the Kernighan-Lin algorithm and the Saab-Rao algorithms for all of the graphs and their algorithm takes less running time than the other algorithms mentioned for all of the graphs with more than 100 nodes. For the simulated annealing approach, they conclude that sequential neighborhood search outperforms random neighborhood search in solving the graph bisectioning problem. >

Algorithm management using genetic search for computer-aided vehicle routing

Abstract: Research into alternative ways of employing artificial intelligence techniques to direct mathematical algorithms is described. The authors have developed a software system called XVRP-GA that demonstrates an integrated framework for this synergism, in the domain of computer-aided vehicle routing and scheduling problems. The system assists researchers and decision makers in applying mathematical algorithms to a specific routing problem instance by intelligently adapting the algorithm to the problem description. The genetic search adaptively refines the parameters that control the work of the underlying algorithm. The resultant solutions are uniformly superior to the best known algorithms working alone. To reduce the computational overhead of genetic search, a mechanism for improving the performance of the search is employed. Several evaluation functions that permit the parallel investigation of multiple peaks in the search space are utilized, resulting in significantly increased efficiency in the genetic search. >

Minimal state space search in parallel production systems

Abstract: A methodology for transforming the original search space into an equivalent but minimal search space is proposed. First, the concept of dependences leads to a procedure for reduction of the search space. The search procedure using this method can produce a minimal and complete search space. It is shown that this method is applicable to parallel search as well. An added advantage of this method is that it does not exclude the use of heuristics. pi - lambda transformation is introduced to reduce the parallel search space. >

Path planning for assembly of strut-based structures

Abstract: A path planning method with collision avoidance for a general single chain nonredundant or redundant robot is proposed. Joint range boundary overruns are also avoided. The result is a sequence of joint vectors which are passed to a trajectory planner. A potential field algorithm in joint space computes incremental joint vectors delta-q = delta-q(sub a) + delta-q(sub c) + delta-q(sub r). Adding delta-q to the robot's current joint vector leads to the next step in the path. Delta-q(sub a) is obtained by computing the minimum norm solution of the underdetermined linear system J delta-q(sub a) = x(sub a) where x(sub a) is a translational and rotational force vector that attracts the robot to its goal position and orientation. J is the manipulator Jacobian. Delta-q(sub c) is a collision avoidance term encompassing collisions between the robot (links and payload) and obstacles in the environment as well as collisions among links and payload of the robot themselves. It is obtained in joint space directly. Delta-q(sub r) is a function of the current joint vector and avoids joint range overruns. A higher level discrete search over candidate safe positions is used to provide alternatives in case the potential field algorithm encounters a local minimum and thus fails to reach the goal. The best first search algorithm A* is used for graph search. Symmetry properties of the payload and equivalent rotations are exploited to further enlarge the number of alternatives passed to the potential field algorithm.

Performance of A* and IDA*-a worst case analysis

Abstract: Summary form only given The paper presents a detailed comparison between algorithms A* and IDA* A* is a best first search algorithm which at each node n in the graph uses a node evaluation function f(n)=g(n)+h(n), where g(n) is the cost of the currently known best path from the start node s to n and h(n) is an estimate of h*(n) (cost of the minimum cost path from n to a goal node) At each iteration, A* selects a node with minimum f-value for expansion A* is known to be optimal in terms of number of node expansions However, the storage requirement of A* is very high-it is exponential in the depth of the solution found The other algorithm, IDA*, unfolds a graph into a tree In every iteration, IDA* starts the search from the start node and makes a depth first search within the current threshold The paper presents a necessary and sufficient condition for the O(N) time complexity of IDA* It shows that IDA* can have O(N) time complexity under a more general condition than that was originally felt Moreover, it illustrates through examples, that the different conditions imposed in the analysis of IDA* are neither sufficient nor necessary It also shows that the worst case time complexity of IDA* can become O(N/sup 2/) for tree searches >

Wave-shaping in Multiprocessor Bidirectional Heuristic State Space Search

Abstract: Parallel bidirectional heuristic island search combines forward chaining, backward chaining, and parallelism to search a state space. The only algorithm in this category to date (PBA*) has been demonstrated to exhibit excellent performance in practice (superlinear speedup in all tested cases) [Nels90d]. This paper introduces the concept of wave-shaping for parallel bidirectional heuristic island search. The resulting algorithm improves the performance of PBA* by dynamically redirecting the local search processes that run concurrently on PBA*, toward quick path establishment. Experimental results on a uniprocessor, as well as a multiprocessor machine (Intel iPSC/2 hypercube) demonstrate the viability of the proposed method.

Controlled best-first search for near optimal solutions

Abstract: Many heuristic search algorithms are available for solving combinatorial optimization problems in artificial intelligence and operations research applications. However, most of these algorithms do not scale up in practice because of their time and/or storage limitations. The paper presents two algorithms, namely BDA* (breadth-depth-A*) and CA* (controlled A*), which can overcome both time and storage limitations at the expense of not guaranteeing optimal solutions at all times. The paper demonstrates the working of BDA* and CA* on the well known state space problems, namely 15-puzzle and 3-machine flow-shop scheduling problem. A new inadmissible heuristic is suggested for sliding tile puzzles. Detailed experimental results showing the effectiveness of the algorithms are also presented. >