A Prufer-based genetic algorithm for allocation of the vehicles in a discounted transportation cost system

Hashemi, Zahrasadat; Tari, Farhad Ghassemi

doi:10.1080/23302674.2016.1226980

Cited by 7 publications

(7 citation statements)

References 19 publications

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“…Prüfer sequences have been used in the past as a sequential representation of tree structures in stochastic search methods (Molla-Alizadeh-Zavardehi et al 2011;Hashemi and Tari 2018), problems in fuzzy systems (Nayeem and Pal 2013;Xu et al 2008), and hierarchical or graph data management (Yang and Wang 2020;Pradhan and Bhattacharya 2019). To the best of our knowledge, our work is the first effort in using Prüfer sequences and exploiting their unique properties in (deep) representation learning for structured data.…”

Section: Abstract Syntax Trees and Pr üFer Sequencesmentioning

confidence: 99%

Code Representation Learning with Prüfer Sequences

Jinpa

Gao

2021

Preprint

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An effective and efficient encoding of the source code of a computer program is critical to the success of sequence-to-sequence deep neural network models for tasks in computer program comprehension, such as automated code summarization and documentation. A significant challenge is to find a sequential representation that captures the structural/syntactic information in a computer program and facilitates the training of the learning models. In this paper, we propose to use the Prüfer sequence of the Abstract Syntax Tree (AST) of a computer program to design a sequential representation scheme that preserves the structural information in an AST. Our representation makes it possible to develop deep-learning models in which signals carried by lexical tokens in the training examples can be exploited automatically and selectively based on their syntactic role and importance. Unlike other recently-proposed approaches, our representation is concise and lossless in terms of the structural information of the AST. Empirical studies on real-world benchmark datasets, using a sequenceto-sequence learning model we designed for code summarization, show that our Prüfer-sequence-based representation is indeed highly effective and efficient, outperforming significantly all the recently-proposed deeplearning models we used as the baseline models.

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Section: Abstract Syntax Trees and Pr üFer Sequencesmentioning

confidence: 99%

Code Representation Learning with Prüfer Sequences

Jinpa

Gao

2021

Preprint

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“…Continue take the above tree T as an example. The PruferSeq that we get is [3,1,5,5,1], according to step_1, construct the SeqtSeq: [1,2,3,4,5,6,7], the leftmost sequence number that in SeqtSeq but not in PruferSeq is 2, and the leftmost number of the SeqtSeq is 3, so rebuild edge (2,3), as shown in According to the decoding step_3, the above processes will be repeated until SeqtSeq has only two numbers left: [1,7], finally rebuild the edge (1,7). The changes of SeqtSeq and PruferSeq are shown below.…”

Section: B Prufer Decoding Of Rootless Labeled Treementioning

confidence: 99%

“…Prufer sequence is not incredibly widely used, but it can be applied on some special occasions, such as be integrated into the design of the Genetic Algorithm (GA) [4][5][6] and used to solve the Minimum Spanning Tree (MST) problem [7] . Reference [8] proposes a new XML schema matching framework based on the use of prufer encoding to improve the performance of identifying and discovering complex matches.…”

Section: Introductionmentioning

confidence: 99%

Prufer Coding: A Vectorization Method for Undirected Labeled Graph

Yang

Wang

2020

IEEE Access

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Prufer algorithm is a powerful method for topology vectorization, but the traditional prufer algorithm method can only encode a rootless labeled tree, and no prior work has studied the method of applying it to the graph vectorization. This paper proposes a vectorization method for undirected labeled graphs based on the prufer algorithm, including graph encoding and decoding algorithms. A particular case was discovered by preliminary experiments, which will reduce the accuracy of the coding algorithm (when the node size reaches more than 150, the accuracy can only reach about 60%), so a connectivity check mechanism that based on the Warshall algorithm is proposed and added to the coding algorithm. A large number of experimental verifications show that the accuracy of the coding algorithm can reach 100% after introducing this mechanism. Then the length of the vector generated by the coding algorithm is analyzed, and the results show that graph vectorization can improve the efficiency of partial topology calculation. Finally, the defects of the algorithm are discussed. The most significant defect is that the length of the vector generated by the encoding algorithm is uncertain, which will prevent it from being applied to more topological calculations.

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“…In our opinion, the issue of optimal routing should include the safety component, but the paper's authors did not take it into consideration. Study [18] addressed the distribution of various types of vehicles for transporting products from a manufacturing enterprise to its warehouses. The issue relates to a limited number of vehicles of different capacities with constant and variable costs, as well as to the mechanism of discounts.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Determining U 7 typically employs using the Dijkstra method [18], which is based on determining the shortest path at each stage from formula…”

Section: Fig 1 Example Of a Road Network Connecting Points A And Bmentioning

confidence: 99%

Minimization of transportation risks in logistics by choosing a cargo delivery route with the minimal projected number of road accidents

Oklander

Yashkina²,

Yashkin³

2019

EEJET

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A Prufer-based genetic algorithm for allocation of the vehicles in a discounted transportation cost system

Cited by 7 publications

References 19 publications

Code Representation Learning with Prüfer Sequences

Code Representation Learning with Prüfer Sequences

Prufer Coding: A Vectorization Method for Undirected Labeled Graph

Minimization of transportation risks in logistics by choosing a cargo delivery route with the minimal projected number of road accidents

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