In Individual class, we define a class to describe a solution. In class Population, we define a class to represent a set of solutions. We implement 4 different mutation operators (insert, swap, inversion, scramble) in class mutation, 4 crossover operators (Order Crossover, PMX Crossover, Cycle Crossover, Edge Recombination) in class crossOver and 3 selection operators (fitness-proportional, tournament selection, elitism) in class Selection. In class Algorithms, we combine different selection, crossover and mutation algorithms to form three evolutionary algorithms. TSPProblem.py contains class TSPProblem, which represents the TSP problem and enables the construction and visualization of TSP problems. Read_data.py contains class Read_data, functioning as a data loader. The folder Experiment1_logfiles consists of the log-files containing the results generated by the code. The folder dataSet consists of the problem inputs and optimal outputs of the symmetric traveling salesperson problem of the TSPlib. The test*.py and FinalTest*.py are to be runned to obtain resolutions. The walking trajectory can be obtained by running TSPProblem.py. We integrated the operations for this experiment in FinalTest1_.py and FinalTest2.py run FinalTest1_.py ,you can get .txt and log files with the answer for Experiment1 run FinalTest2.py ,you can get .txt and log files with the answer for Experiment2 run test3.py , you can get txt that show the length according to the opt_tour file
presentation video link:https://www.bilibili.com/video/BV13U4y1n71E/
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