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Write a research paper on “Using Genetic Algorithm to Solve the Traveling Salesman Problem (TSP)”. Your paper outlining the results of your research should be 10 pages (including references; a single...

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Write a research paper on “Using Genetic Algorithm to Solve the Traveling Salesman Problem (TSP)”. Your paper outlining the results of your research should be 10 pages (including references; a single space; MS Word) and should include three main components:

1. Description [20%]: Describe Genetic Algorithm (parallel algorithm) and briefly share its history and discovery, and then provide a detailed explanation of how it works.

2. Analysis [40%]: Analyze the algorithm and consider questions such as the following: Is it efficient? What is the algorithm’s complexity? [You can use Big O()], does it handle large input, how does it compare to other TSP algorithms like BFS and DFS.

3. Observations [40%]: Finally, summarize what you’ve learned about Genetic Algorithm and what you’ve learned about the traveling salesman problem as a whole and include any interesting observations you’ve made.

Make sure to cite all your sources!

Answered Same Day Nov 08, 2021

Solution

Neha answered on Nov 18 2021
140 Votes
Genetic Algorithm
Genetic algorithms come under evolutionary techniques and these techniques can be used for optimization purposes as per the survival of the fittest idea. These techniques do not give any surety for the optimal solutions but they can be used to get good approximation. These algorithms are useful for travelling salesman problem which is a part of NP-hard problems. The genetic algorithm is dependent over the selection criteria, crossover and mutation operators. There are various representation techniques such as binary, path, adjacency, matrix and ordinal to tackle the travelling salesman problem.
Genetic algorithms (GAs) are derivative-free stochastic approach based on biological evolutionary processes proposed by Holland [1]. There are many applications which are done using GA. One of these application is population of chromosomes which are represented by some parameters set codes. The travelling salesman problem is one of the most significant problem which was documented by Euler in 1759 [2]. It is a fundamental problem and is included in the computer science field. It can be described as getting the minimum total distance travelled by the sales person by touring all cities and returning to the initial city for exactly once. On the basis of the distance matrix TSP can be classified as symmetric and asymmetric. There are multiple possible ways to find the tour as soon as we fix the initial city for asymmetric but same is not applied for the symmetric method. This is the main reason why we call TSP a NP-hard problem. There are many applications of TSP such as routing, scheduling problems. X-ray crystallography, movement of people, Very-large scale integrated circuits [3].
Here are the steps for a simple genetic algorithm
1) Create an initial population of X chromosomes.
2) Each chromosome will get under the fitness evaluation.
3) X/2 parents of the cu
ent population will be chosen via proportional selection.
4) To create the offspring using a crossover operator we can chose two parents randomly.
5) Apply mutation operators
6) Steps 4 and 5 are repeated until all the parents are selected and mated.
7) After getting new one old chromosomes will be replaced by them.
8) Evaluate fitness of each.
9) The test will stop if we get number of generations equal to some upper bound else repeat from step 3.
For the genetic algorithm selection criteria, crossover and mutation are important but crossover is much more important and plays a vital role. There are many crossover operators with significant importance.
Genetic algorithm is the result of research made by John Holland (Sablier) but it became popular in 90s. The main reason to find such algorithm was for solving problems where deterministic algorithms are too costly. This algorithm works in the areas where traditional ways do not behave efficiently. For example, genetic...
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