Solving Graph Matching with EDAs Using a Permutation-Based Representation

2002 ◽  
pp. 243-265 ◽  
Author(s):  
E. Bengoetxea ◽  
P. Larrañaga ◽  
A. Perchant ◽  
I. Bloch
Keyword(s):  
Graph Matching

User Manual and Installation Guide for the Graph Matching Toolkit (GMT) Version 1.0

2014 ◽  
Author(s):  
Heather Roy ◽  
Kirk Ogaard ◽  
Sue Kase
Keyword(s):  
Graph Matching

Structural information and (hyper)graph matching for MRI piglet brain extraction

2019 ◽  
Author(s):  
A. Durandeau ◽  
J.-B. Fasquel ◽  
I. Bloch ◽  
E. Mazerand ◽  
P. Menei ◽  
...  
Keyword(s):  
Graph Matching ◽  
Structural Information ◽  
Brain Extraction

scholarly journals SIGMA: A Semantic Integrated Graph Matching Approach for identifying reused functions in binary code

2015 ◽  
Vol 12 ◽  
pp. S61-S71 ◽  
Author(s):  
Saed Alrabaee ◽  
Paria Shirani ◽  
Lingyu Wang ◽  
Mourad Debbabi
Keyword(s):  
Binary Code ◽  
Graph Matching

scholarly journals Graph matching as a graph convolution operator for graph neural networks

2021 ◽  
Author(s):  
Maxime Martineau ◽  
Romain Raveaux ◽  
Donatello Conte ◽  
Gilles Venturini
Keyword(s):  
Neural Networks ◽  
Convolution Operator ◽  
Graph Matching ◽  
Graph Neural Networks

A new approach based on graph matching and evolutionary approach for sport scheduling problem

2020 ◽  
pp. 1-16
Author(s):  
Meriem Khelifa ◽  
Dalila Boughaci ◽  
Esma Aïmeur
Keyword(s):  
Graph Matching ◽  
State Of The Art ◽  
Travel Cost ◽  
Round Robin ◽  
New Approach ◽  
Traveling Tournament Problem ◽  
Significant Interest ◽  
National League ◽  
Better Than

The Traveling Tournament Problem (TTP) is concerned with finding a double round-robin tournament schedule that minimizes the total distances traveled by the teams. It has attracted significant interest recently since a favorable TTP schedule can result in significant savings for the league. This paper proposes an original evolutionary algorithm for TTP. We first propose a quick and effective constructive algorithm to construct a Double Round Robin Tournament (DRRT) schedule with low travel cost. We then describe an enhanced genetic algorithm with a new crossover operator to improve the travel cost of the generated schedules. A new heuristic for ordering efficiently the scheduled rounds is also proposed. The latter leads to significant enhancement in the quality of the schedules. The overall method is evaluated on publicly available standard benchmarks and compared with other techniques for TTP and UTTP (Unconstrained Traveling Tournament Problem). The computational experiment shows that the proposed approach could build very good solutions comparable to other state-of-the-art approaches or better than the current best solutions on UTTP. Further, our method provides new valuable solutions to some unsolved UTTP instances and outperforms prior methods for all US National League (NL) instances.

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