How does the genetic algorithm perform with default parameters?

Conditions

• The genetic-algorithm implementation is run with the displayed default parameters.
• The problem instance is the one shown in the visualization.
• The word "efficiently" is qualitative here; no formal efficiency metric or runtime bound is provided.

Reasoning, step by step

1. Run the genetic algorithm with default parameters (e.g., population size 1000, greedy crossover true).
2. Observe the animation of the tour refining.
3. Note the decreasing path length in the status bar.
4. Conclude that the algorithm performs well for this instance.

Example

The narrator says, "You can see the genetic algorithm can efficiently solves the traveling salesman problem using the default parameters." The genetic-programming page runs with visible parameters including Iterations: 100, population size: 1000, greedy crossover: true, and numberOfCities: 30.

Common misconceptions

• Assuming that the genetic algorithm always finds the absolute optimal solution.
• Thinking that default parameters are sufficient for all TSP instances regardless of size.

Watch the explanation

Explore next

• What does greedy crossover do in the genetic-algorithm TSP demo?
• How large should the genetic-algorithm population be according to the video?
• Why does the video say population size 50 does not get a good solution?