Solving the Traveling Salesman Problem Using Genetic Algorithms
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In this article, we demonstrate how to solve the Traveling Salesman Problem (TSP) using genetic algorithms. The TSP is a classic combinatorial optimization problem aimed at finding the shortest possible route that visits each city exactly once and returns to the origin city. Genetic algorithms are heuristic search techniques inspired by natural evolution, utilizing operations like selection, crossover, and mutation to explore the solution space efficiently. Our implementation includes key components such as population initialization, fitness evaluation using total path distance, tournament selection for parent choice, ordered crossover (OX) for route recombination, and swap mutation for diversity introduction. Below we provide a functional Python code example with detailed comments explaining each algorithmic step, enabling you to understand and customize the implementation for various TSP instances. Let's begin!
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