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README.md

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# Traveling salesman problem with genetic algorithm
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## Description
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This code was implemented for the graph discipline the objective is to resolve the traveling salesman problem using a genetic algorithm.
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## Usage
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To run the code:
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```
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pythom3 main.py
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```
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## Settings
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These are the initial settings of the code you can change them according to your specifications.
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* Population size: 10
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* Probability of mutation: 0.5
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* Crossover: Is defined by function **mutatedGene** which makes a random change between tho cities.
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## Result
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```
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Current temp: 10000
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Geração 1
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GNOME Valor Fitness
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0121213651412120 802
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0121213611452120 773
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0121123651412120 769
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0123211651412120 753
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0121213651412120 802
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0121213651412120 802
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0121213651412120 802
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0151213621412120 752
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0126213151412120 694
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0121613251412120 768
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Current temp: 9000.0
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Geração 2
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GNOME Valor Fitness
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0126212151412130 667
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0151213621412120 752
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0213211651412120 784
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0121613252412110 676
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0121143651212120 796
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0121413611252120 741
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0121213251416120 768
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0121213654112120 791
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0521213611412120 764
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0123211651412120 753
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Current temp: 8100.0
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Geração 3
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GNOME Valor Fitness
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0126212151412130 667
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0121615232412110 686
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0121413116252120 683
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0151213621112420 650
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0123411651212120 780
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0421213611512120 773
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0121213151426120 718
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0211211653412120 737
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0121213654111220 681
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0121143151212620 688
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Current temp: 7290.0
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Geração 4
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GNOME Valor Fitness
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0151213621112420 650
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0126214151212130 667
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0121211654131220 698
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0121413116252120 683
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0121615232112140 734
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0124113151212620 643
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0123211151426120 652
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0511211623412120 710
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0426213111512120 640
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0121411651232120 753
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Current temp: 6561.0
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Geração 5
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GNOME Valor Fitness
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0421213161512120 790
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0124113151212620 643
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0121213621115420 690
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0122311151426120 575
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0126214351212110 616
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0121413216152120 768
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0621211154131220 648
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0511211223416120 675
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0121615232121140 734
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0121411651232210 645
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```
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