Update README.md

Author: Yuhao-C

README.md

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 # TSP_GeneticAlgorithm
 
+## Demo
+![2021-09-04 155638](https://user-images.githubusercontent.com/32063307/132106538-20b92cbd-b9ab-4bcf-963d-f2f25826a3c4.gif)
+
+## Introduction
+
 This Repo saves my attemp to solve the Travelling Salesman Problem with Genetic Algorithm.
 
 Although the structure of Genetic Algorithm (GA) are almost the same for solving different problems, their parameters vary a lot, including “population size”, “fitness function”, “type of crossover & mutation operators”, “crossover rate”, and “mutation rate”. Since the invention of the genetic algorithm, a lot of efforts have been put to understand how each parameter will affect the performance of the genetic algorithm, but no conclusive and definite conclusion has been drawn. In other words, the effect of these parameters on the performance is local to each specific problem.
 
 Travelling Salesman Problem (TSP) is an NP-hard  problem in the field of combinatorial optimization. A genetic algorithm can be used to solve this problem. In this project, the impact of different mutation rates on the performance of genetic algorithm will be identified in order to find out the most preferable value for this problem by conducting the controlled experiment.
-
-## Demo
-![2021-09-04 155638](https://user-images.githubusercontent.com/32063307/132106538-20b92cbd-b9ab-4bcf-963d-f2f25826a3c4.gif)