Heuristic search algorithms for lot streaming in a two-machine flowshop

2005 ◽  
Vol 27 (1-2) ◽  
pp. 174-180 ◽  
Author(s):  
S. Marimuthu ◽  
S.G. Ponnambalam
Keyword(s):  
Heuristic Search ◽  
Search Algorithms ◽  
Lot Streaming ◽  
Heuristic Search Algorithms

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 Efficient heuristic search algorithms for soft-decision decoding of linear block codes

1998 ◽  
Vol 44 (7) ◽  
pp. 3023-3038 ◽  
Author(s):  
Ching-Cheng Shih ◽  
C.R. Wulff ◽  
C.R.P. Hartmann ◽  
C.K. Mohan
Keyword(s):  
Heuristic Search ◽  
Search Algorithms ◽  
Block Codes ◽  
Soft Decision ◽  
Linear Block Codes ◽  
Heuristic Search Algorithms ◽  
Soft Decision Decoding ◽  
Linear Block

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.

Fitness-distance balance (FDB): A new selection method for meta-heuristic search algorithms

2020 ◽  
Vol 190 ◽  
pp. 105169 ◽  
Author(s):  
Hamdi Tolga Kahraman ◽  
Sefa Aras ◽  
Eyüp Gedikli
Keyword(s):  
Heuristic Search ◽  
Selection Method ◽  
Search Algorithms ◽  
Heuristic Search Algorithms

scholarly journals Machine Learning Based Heuristic Search Algorithms to Solve Birds of a Feather Card Game

2019 ◽  
Vol 33 ◽  
pp. 9656-9661
Author(s):  
Bryon Kucharski ◽  
Azad Deihim ◽  
Mehmet Ergezer
Keyword(s):  
Machine Learning ◽  
Undergraduate Students ◽  
Heuristic Search ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Perfect Information ◽  
Monte Carlo Tree Search ◽  
Heuristic Search Algorithms ◽  
Heuristic Search Algorithm

This research was conducted by an interdisciplinary team of two undergraduate students and a faculty to explore solutions to the Birds of a Feather (BoF) Research Challenge. BoF is a newly-designed perfect-information solitaire-type game. The focus of the study was to design and implement different algorithms and evaluate their effectiveness. The team compared the provided depth-first search (DFS) to heuristic algorithms such as Monte Carlo tree search (MCTS), as well as a novel heuristic search algorithm guided by machine learning. Since all of the studied algorithms converge to a solution from a solvable deal, effectiveness of each approach was measured by how quickly a solution was reached, and how many nodes were traversed until a solution was reached. The employed methods have a potential to provide artificial intelligence enthusiasts with a better understanding of BoF and novel ways to solve perfect-information games and puzzles in general. The results indicate that the proposed heuristic search algorithms guided by machine learning provide a significant improvement in terms of number of nodes traversed over the provided DFS algorithm.

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