Can the traveling salesman problem be solved using a genetic algorithm?

Can the traveling salesman problem be solved using a genetic algorithm?

The genetic algorithm depends on selection criteria, crossovers and mutation operators. To solve the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations.

How can we solve the genetic algorithm problem?

When and how to solve problems with genetic algorithms

  1. Determine the problem and the goal.
  2. Break the solution down into smaller properties (genomes)
  3. Build a population by randomizing said properties.
  4. Evaluate each unit of the population.
  5. Selective breed (choosing genomes from each parent)
  6. Rinse and repeat.

Which algorithm is best suited to the traveling salesman problem?

A new hybrid cultural algorithm with local search (HCALS) is introduced to solve the traveling salesman problem (TSP).

What is a genetic algorithm with example?

A genetic algorithm is a search heuristic inspired by Charles Darwin’s theory of natural evolution. This algorithm mirrors the process of natural selection where the fittest individuals are selected for breeding to produce next generation offspring.

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What are the advantages of Genetic Algorithm to solve NP problems?

“Genetic algorithms (GAs) are good at taking large, potentially huge search spaces and iterating through them, looking for optimal combinations of things, solutions that you would struggle to accomplish.” A genetic algorithm (GA) is an iterative search, optimization, and adaptive machine learning technique based on…

Why is the traveling salesman problem so difficult?

It is a well-known algorithmic problem in the fields of computer science and operations research. This means that TSP is classified as NP-hard because it has no “fast” solution and the complexity of calculating the best route will increase as you add more destinations to the problem.

What are the two main characteristics of the genetic algorithm?

The main operators of genetic algorithms are reproduction, crossing and mutation. Reproduction is a process based on the objective function (fitness function) of each string. This objective function identifies the “good” quality of a string.

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What are the steps of Genetic Algorithm?

Five phases are considered in a genetic algorithm:

  1. Initial population.
  2. Fitness function.
  3. Selection.
  4. Crossing.
  5. Mutation.

How are genetic algorithms used to solve the traveling salesman problem?

In this paper, a genetic algorithm is proposed to solve the traveling salesman problem. Genetic algorithms are heuristic search algorithms inspired by the process that sustains the evolution of life. The algorithm is designed to replicate the process of natural selection to ensure generation, i.e. the survival of the fittest beings.

Is the traveling salesman problem a combinatorial problem?

The traveling salesman problem is a classic problem in combinatorial optimization. This problem consists of finding the shortest path that a seller must take to traverse a list of cities and return to the city of origin. The list of cities and the distance between each pair are provided.

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How are algorithms used to solve optimization problems?

These algorithms can be implemented to find a solution to optimization problems of different types. One such problem is the traveling salesman problem. The problem says that a seller is assigned a set of cities, he must find the shortest path to visit each city exactly once and return to the starting city.

How long does it take to solve the traveling salesman problem?

The TSP with 10 cities can be solved by a DP method in almost 0.2 seconds using Intel Core i7. This number increases to almost 13 seconds (~60 times longer) with 15 cities. That is, the time complexity increases dramatically even with a small increase in the number of cities.

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