A phylogenetic analysis of the isopod family Janiridae (Crustacea)

1994 ◽  
Vol 8 (3) ◽  
pp. 749 ◽  
Author(s):  
GDF Wilson
Keyword(s):  
Heuristic Search ◽  
Cladistic Analysis ◽  
Tail Probability ◽  
Search Algorithms ◽  
Data Matrix ◽  
Data Set ◽  
New Family ◽  
Heuristic Search Algorithms ◽  
Family Level ◽  
The Impact

A phylogeny of the isopod family Janiridae and genera from presumptive outgroups, Acanthaspidiidae, Joeropsididae and Microparasellidae is estimated. Characters were gathered from the published literature, and assembled into a data matrix for cladistic analysis. The data, when evaluated with heuristic search algorithms, yielded eight most-parsimonious trees, none of which supported the monophyly of the Janiridae. To evaluate the impact of homoplasy, characters with a rescaled consistency less than 0.1 were deleted, resulting in four somewhat different trees that were non-monophyletic for the janirids. With the smaller data set, trees supporting janirid monophyly were 10 steps longer. A permutation tail probability test found substantially more hierarchical information in the janirid data set than in randomised data. Internal topologies of the shortest trees were evaluated as hypotheses for new family-level groups, although new family-level classifications cannot be proposed at this time owing to insufficient evidence. The Janiridae therefore cannot be considered monophyletic.

scholarly journals Exploiting Obstacle Geometry to Reduce Search Time in Grid-Based Pathfinding

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1186
Author(s):  
Fahed Jubair ◽  
Mohammed Hawa
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Search Time ◽  
Search Tree ◽  
Search Algorithms ◽  
Binary Search ◽  
Binary Search Tree ◽  
Grid Map ◽  
Heuristic Search Algorithms ◽  
The Impact

Pathfinding is the problem of finding the shortest path between a pair of nodes in a graph. In the context of uniform-cost undirected grid maps, heuristic search algorithms, such as A ★ and weighted A ★ ( W A ★ ), have been dominantly used for pathfinding. However, the lack of knowledge about obstacle shapes in a gird map often leads heuristic search algorithms to unnecessarily explore areas where a viable path is not available. We refer to such areas in a grid map as blocked areas (BAs). This paper introduces a preprocessing algorithm that analyzes the geometry of obstacles in a grid map and stores knowledge about blocked areas in a memory-efficient balanced binary search tree data structure. During actual pathfinding, a search algorithm accesses the binary search tree to identify blocked areas in a grid map and therefore avoid exploring them. As a result, the search time is significantly reduced. The scope of the paper covers maps in which obstacles are represented as horizontal and vertical line-segments. The impact of using the blocked area knowledge during pathfinding in A ★ and W A ★ is evaluated using publicly available benchmark set, consisting of sixty grid maps of mazes and rooms. In mazes, the search time for both A ★ and W A ★ is reduced by 28 % , on average. In rooms, the search time for both A ★ and W A ★ is reduced by 30 % , on average. This is achieved while preserving the search optimality of A ★ and the search sub-optimality of W A ★ .

Quantum dialogue protocol based on Grover’s search algorithms

2019 ◽  
Vol 34 (21) ◽  
pp. 1950169
Author(s):  
Aihan Yin ◽  
Kemeng He ◽  
Ping Fan
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Quantum Dialogue ◽  
Quantum Search ◽  
Decoy State ◽  
Quantum Search Algorithm ◽  
Heuristic Search Algorithms ◽  
Security Information

Among many classic heuristic search algorithms, the Grover quantum search algorithm (QSA) can play a role of secondary acceleration. Based on the properties of the two-qubit Grover QSA, a quantum dialogue (QD) protocol is proposed. In addition, our protocol also utilizes the unitary operations and single-particle measurements. The transmitted quantum state (except for the decoy state used for detection) can transmit two-bits of security information simultaneously. Theoretical analysis shows that the proposed protocol has high security.

scholarly journals Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search

2020 ◽  
Vol 34 (06) ◽  
pp. 9827-9834
Author(s):  
Maximilian Fickert ◽  
Tianyi Gu ◽  
Leonhard Staut ◽  
Wheeler Ruml ◽  
Joerg Hoffmann ◽  
...  
Keyword(s):  
Real Time ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Experimental Results ◽  
Data Driven ◽  
Heuristic Search Algorithms ◽  
Original Presentation ◽  
Performance Statistics ◽  
The Cost

Suboptimal heuristic search algorithms can benefit from reasoning about heuristic error, especially in a real-time setting where there is not enough time to search all the way to a goal. However, current reasoning methods implicitly or explicitly incorporate assumptions about the cost-to-go function. We consider a recent real-time search algorithm, called Nancy, that manipulates explicit beliefs about the cost-to-go. The original presentation of Nancy assumed that these beliefs are Gaussian, with parameters following a certain form. In this paper, we explore how to replace these assumptions with actual data. We develop a data-driven variant of Nancy, DDNancy, that bases its beliefs on heuristic performance statistics from the same domain. We extend Nancy and DDNancy with the notion of persistence and prove their completeness. Experimental results show that DDNancy can perform well in domains in which the original assumption-based Nancy performs poorly.

A family of heuristic search algorithms for feature model optimization

2019 ◽  
Vol 172 ◽  
pp. 264-293 ◽  
Author(s):  
Luis Emiliano Sánchez ◽  
Jorge Andrés Diaz-Pace ◽  
Alejandro Zunino
Keyword(s):  
Heuristic Search ◽  
Search Algorithms ◽  
Feature Model ◽  
Model Optimization ◽  
Heuristic Search Algorithms

scholarly journals Effective Heuristics for Suboptimal Best-First Search

2016 ◽  
Vol 57 ◽  
pp. 273-306 ◽  
Author(s):  
Christopher Wilt ◽  
Wheeler Ruml
Keyword(s):  
Sustained Attention ◽  
Heuristic Search ◽  
Rank Correlation ◽  
Search Algorithms ◽  
Optimal Solutions ◽  
Heuristic Function ◽  
Optimal Search ◽  
Heuristic Search Algorithms ◽  
Best First Search ◽  
Goal Distance

Suboptimal heuristic search algorithms such as weighted A* and greedy best-first search are widely used to solve problems for which guaranteed optimal solutions are too expensive to obtain. These algorithms crucially rely on a heuristic function to guide their search. However, most research on building heuristics addresses optimal solving. In this paper, we illustrate how established wisdom for constructing heuristics for optimal search can fail when considering suboptimal search. We consider the behavior of greedy best-first search in detail and we test several hypotheses for predicting when a heuristic will be effective for it. Our results suggest that a predictive characteristic is a heuristic's goal distance rank correlation (GDRC), a robust measure of whether it orders nodes according to distance to a goal. We demonstrate that GDRC can be used to automatically construct abstraction-based heuristics for greedy best-first search that are more effective than those built by methods oriented toward optimal search. These results reinforce the point that suboptimal search deserves sustained attention and specialized methods of its own.

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