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Greedy, genetic, and greedy genetic algorithms for the quadratic knapsack problem
2005
Proceedings of the 2005 conference on Genetic and evolutionary computation - GECCO '05
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The greedy heuristics do well, as does the naive GA, but the greedy GA exhibits the best performance. ...
Two genetic algorithms encode candidate selections of objects as binary strings and generate only strings whose selections of objects have total weight no more than the knapsack's capacity. ...
We illustrate this general observation with greedy, genetic, and greedy genetic algorithms for the Quadratic Knapsack Problem. ...
doi:10.1145/1068009.1068111
dblp:conf/gecco/Julstrom05a
fatcat:gatoyxfifbbgjokc35dr3s6y3u
K-Means Genetic Algorithms with Greedy Genetic Operators
2020
Mathematical Problems in Engineering
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In such cases, genetic algorithms with greedy agglomerative heuristic crossover operator might be a good choice. ...
by the genetic algorithms for large-scale k-means problems. ...
In Section 4, we propose new modifications to the genetic algorithms with greedy heuristic crossover operator. ...
doi:10.1155/2020/8839763
fatcat:qtvocyrabrhargdptp7bykoxba
Parallel Search Strategies for TSPs Using a Greedy Genetic Algorithm
[chapter]
2008
Greedy Algorithms
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Compared to the GA, the greedy genetic algorithm with improved genetic operations has been presented for the global optimization of TSPs. ...
In the next a few sections, we present the greedy genetic algorithm (GGA), how we modify a genetic algorithm to solve TSP, our methodology, results, and conclusions. ...
Parallel Search Strategies for TSPs Using a Greedy Genetic Algorithm, Greedy Algorithms, Witold Bednorz (Ed.), ISBN: 978-953-7619-27-5, InTech, Available from: http://www.intechopen.com/books/greedy_algorithms ...
doi:10.5772/6342
fatcat:fkal6ltzg5gzpamubunpxe6gsm
Greedy genetic algorithm to Bounded Knapsack Problem
2010
2010 3rd International Conference on Computer Science and Information Technology
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GREEDY GENETIC ALGORITHM (GGA) The basic idea of the flow of genetic algorithm for the bounded knapsack is shown in Figure 1 . A Greedy operator is used to ensure the feasibility of the solution. ...
The selection operator with greedy algorithm improves the searching ability of the genetic algorithm. ...
doi:10.1109/iccsit.2010.5563867
fatcat:mofyvpsq25e3bk7am2bgdztoxy
A greedy genetic algorithm for the quadratic assignment problem
2000
Computers & Operations Research
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The genetic algorithm incorporates many greedy principles in its design and, hence, we refer to it as a greedy genetic algorithm. ...
The ideas we incorporate in the greedy genetic algorithm include (i) generating the initial population using a randomized construction heuristic; (ii) new crossover schemes; (iii) a special purpose immigration ...
Acknowledgements We thank Bezalel Gavish for suggesting us to investigate genetic algorithms for QAP. ...
doi:10.1016/s0305-0548(99)00067-2
fatcat:yodorpvxa5cexdbgdgem5twtha
Solving Travelling Salesman Problem Using Greedy Genetic Algorithm GGA
2017
International Journal of Engineering and Technology
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Genetic Algorithm is used to solve these problems and the performance of genetic algorithm depends on its operators. In this paper new greedy genetic algorithm has been proposed to solve TSP. ...
The proposed greedy genetic algorithm search deeper in the search space and find better solutions as compared to existing algorithms. ...
In this paper greedy approach has been used in genetic algorithm. ...
doi:10.21817/ijet/2017/v9i2/170902188
fatcat:lhyk6vfbsjahfnjnb3cxhoz57q
Extracting Minimum Unsatisfiable Cores with a Greedy Genetic Algorithm
[chapter]
2006
Lecture Notes in Computer Science
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In this paper, we propose an efficient greedy genetic algorithm to derive an exact or nearly exact minimum unsatisfiable core. ...
The first two steps are the same as those of the greedy genetic algorithm; 2. ...
We combine the greedy algorithm and the genetic algorithm to make up for the shortage of each other. ...
doi:10.1007/11941439_89
fatcat:ycwiq3fwwze7rlsm735qqomkqy
Genetic and Memetic Algorithm with Diversity Equilibrium based on Greedy Diversification
[article]
2017
arXiv
pre-print
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It confronts the diversity problem using the named greedy diversification operator. ...
The lack of diversity in a genetic algorithm's population may lead to a bad performance of the genetic operators since there is not an equilibrium between exploration and exploitation. ...
Genetic algorithm with diversity equilibrium based on greedy diversification. ...
arXiv:1702.03594v1
fatcat:ndfpmigrpvftrar5keyzwczrzq
Species Choice for Comparative Genomics: Being Greedy Works
2005
PLoS Genetics
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Several projects investigating genetic function and evolution through sequencing and comparison of multiple genomes are now underway. ...
Our mathematical formalization of this problem surprisingly shows that the best long-term cooperative strategy coincides with the seemingly shortterm "greedy" strategy of always choosing the next best ...
FP derived the proof of correctness of the greedy algorithm. ...
doi:10.1371/journal.pgen.0010071
pmid:16327885
pmcid:PMC1298936
fatcat:zbne5dt2b5cxhcrz54hii7qreu
Species Choice for Comparative Genomics: Being Greedy Works
2005
PLoS Genetics
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Several projects investigating genetic function and evolution through sequencing and comparison of multiple genomes are now underway. ...
Our mathematical formalization of this problem surprisingly shows that the best long-term cooperative strategy coincides with the seemingly shortterm "greedy" strategy of always choosing the next best ...
FP derived the proof of correctness of the greedy algorithm. ...
doi:10.1371/journal.pgen.0010071.eor
fatcat:wopd4svoqfhhxp7w52xhw3okam
Data Source Selection Based on an Improved Greedy Genetic Algorithm
2019
Symmetry
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We proposed an improved greedy genetic algorithm (IGGA) to solve the problem of source selection, and carried out a wide range of experimental evaluations on the real and synthetic dataset. ...
• We propose an improved novel greedy genetic algorithm(IGGA). ...
Table 6 . 6 Performance comparison of improved greedy genetic algorithm (IGGA), DGGA, BPSO and ACA. ...
doi:10.3390/sym11020273
fatcat:utkwrjcayfh6hk7g3fdjzta3t4
Applying greedy genetic algorithm on 0/1 multiple knapsack problem
2018
International Journal of Advanced Technology and Engineering Exploration
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In this paper MKP is solved using greedy genetic algorithm. The proposed genetic algorithm uses greedy approach in its selection and reproduction operations of GA. ...
Keywords Multiple knapsack problem, Genetic algorithm, Greedy approach. ...
algorithm Results for solving p01 MKP problem using the greedy genetic algorithmTable 4Results comparison using standard genetic algorithm and greedy genetic algorithm Multiple knapsack instance -p01 ...
doi:10.19101/ijatee.2018.545018
fatcat:tzxnc46v5ja3lmkqtnmxsdwawe
Greedy-Genetic Algorithm Based Video Data Scheduling Over 5G Networks
2022
Intelligent Automation and Soft Computing
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Initially, a Priority scheduling Technique based on the greedy and genetic algorithm is proposed for offering better QOS in reduced time durations. ...
Genetic Algorithm is used for Optimizing the identified problems by means of relying on the selection attributes. ...
Greedy Genetic Scheduling Genetic algorithms are able-bodied and ill-fitted to the analytic video frames-based scheduling problems as the clashing heuristic methods genetic algorithms accomplish on a citizenry ...
doi:10.32604/iasc.2022.020625
fatcat:j4ubqyh6ufa23atxqgbqgsjofa
A Greedy Genetic Algorithm for the TDMA Broadcast Scheduling Problem
2013
IEICE transactions on information and systems
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We present three heuristic genetic operators, including a greedy crossover and two greedy mutation operators, to optimize both objectives of the BSP. ...
In this work, we propose a greedy genetic algorithm for solving the BSP with a large number of nodes. ...
In this paper, we propose a greedy genetic algorithm for the BSP. In our algorithm, we propose three greedy genetic operators, including a crossover and two mutation operators. ...
doi:10.1587/transinf.e96.d.102
fatcat:ammf2m6sercsxpiq67q2ekseja
A NOVEL GREEDY GENETIC ALGORITHM TO SOLVE COMBINATORIAL OPTIMIZATION PROBLEM
2020
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In this paper, a modified genetic algorithm based on greedy sequential algorithm is presented to solve combinatorial optimization problem. ...
The algorithm proposed here is a hybrid of heuristic and computational intelligence algorithm where greedy sequential algorithm is used as operator inside genetic algorithm like crossover and mutation. ...
THE PROPOSED APPROACH The proposed approach novel greedy genetic algorithm (NGGA) combines genetic algorithm (GA) and greedy sequential algorithm (GSA) to solve the GCP. ...
doi:10.5194/isprs-archives-xliv-4-w3-2020-117-2020
fatcat:3xycfwcenbajrctfgfr7faxhfi
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