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1
An algorithm for solving bond pricing problem
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Genetic Algorithm Analysis using the Graph Coloring Method for Solving the University Timetable Problem
Published 2018“…Genetic algorithms were successfully useful to solve many optimization problems including the university Timetable Problem. …”
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Topics in graph algorithms
Published 2003“…This is achieved by implementing some algorithms for the vertex cover problem, and conducting experiments on real data sets. …”
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4
An evolutionary algorithm for solving the geometrically constrained site layout problem
Published 2017“…This paper presents an investigation of applying an evolutionary approach to optimally solve the aforementioned layout problem. The proposed algorithm is two-phases: an initialization phase that generates an initial population of layouts through a sequence of mutation operations, and a reproduction phase that evolve the layouts generated in phase one through a sequence of genetic operations aiming at finding an optimal layout. …”
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5
Mapping realistic data sets on parallel computers
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Scalable parallel algorithms for dynamic programming on tree decomposition. (c2017)
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7
Efficient convex-elastic net algorithm to solve the Euclideantraveling salesman problem
Published 1998“…This paper describes a hybrid algorithm that combines an adaptive-type neural network algorithm and a nondeterministic iterative algorithm to solve the Euclidean traveling salesman problem (E-TSP). …”
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Modified clarke wright algorithms for solving the realistic vehicle routing problem
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9
Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network
Published 2024“…Additionally, the multi-objective genetic algorithm (MOGA) was utilised to extract the most optimal parameters for the injection moulding process, aiming to minimise shear and residual stress and thereby increase the resistance of the final product. …”
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Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts
Published 2022“…In order to benefit from multiple different classifiers, and exploit their strengths, the use of ensembling algorithms arises, which combine the results of the individual classifiers into a final result to achieve greater precision and thus a better result. …”
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Genetic Algorithm for Solving Site Layout Problem with Unequal-Size and Constrained Facilities
Published 2002“…This paper presents an investigation of the applicability of a genetic approach for solving the construction site layout problem. This problem involves coordinating the use of limited site space to accommodate temporary facilities so that transportation cost of materials is minimized. …”
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12
Variable Selection in Data Analysis: A Synthetic Data Toolkit
Published 2024“…Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). …”
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13
Automatic keyword extraction from a real estate classifieds data set
Published 2011“…Keywords are considered to be a solution to this problem and are now widely used to search the information over the internet.In this project we analyze a real estate classifieds data set, with an objective to find keywords that represent this data set. …”
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14
Data reductions and combinatorial bounds for improved approximation algorithms
Published 2016“…Kernelization algorithms in the context of Parameterized Complexity are often based on a combination of data reduction rules and combinatorial insights. …”
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15
Allocating data to distributed-memory multiprocessors by genetic algorithms
Published 2016“…We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. …”
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Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm
Published 2024“…Among these, K-means is widely used for efficiently solving clustering problems. …”
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18
Physical optimization algorithms for mapping data to distributed-memory multiprocessors
Published 1992“…We also present a technique for efficient mapping of large data sets. The algorithms include a parallel genetic algorithm (PGA), a parallel neural network algorithm (PNN) and a parallel simulated annealing algorithm (PSA). …”
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19
Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing
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20
Hardening the ElGamal cryptosystem in the setting of the second group of units. (c2011)
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