In this video, we tackle the classic Travelling Salesman Problem (TSP) using a Genetic Algorithm (GA). TSP is a well-known NP-hard problem where the goal is to find the shortest possible route that visits each city exactly once and returns to the starting point. Watch how evolutionary techniques like selection, crossover, and mutation help solve this complex problem efficiently!

What You’ll Learn:

What is the Travelling Salesman Problem?

How to represent TSP for a Genetic Algorithm

Chromosome structure and fitness function

Selection, crossover (e.g., order crossover), and mutation strategies

Full step-by-step implementation or visual simulation

Great for students, AI enthusiasts, and developers interested in real-world optimization and intelligent systems.

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The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution. You can apply the genetic algorithm to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, nondifferentiable, stochastic, or highly nonlinear. The genetic algorithm can address problems of mixed integer programming, where some components are restricted to be integer-valued.

This flow chart outlines the main algorithmic steps.