Experimenting with Reinsertion
Explore how various reinsertion strategies affect genetic algorithm performance on scheduling problems in Elixir. Understand pure, elitist, and uniform reinsertion, and see their impact on convergence and efficiency to inform your algorithm design decisions.
Applying the different reinsertion strategies
To see the impact of each of these reinsertion strategies in action, we can apply them to the scheduling problem we implemented earlier in this chapter to see how the outcomes differ.
For easier interpretation of the results, the class table has been provided below:
Class Weights
Class | Credit Hours | Difficulty | Usefulness | Interest |
Algorithms | 3.0 | 8.0 | 8.0 | 8.0 |
Artificial Intelligence | 3.0 | 9.0 | 9.0 | 8.0 |
Calculus | 3.0 | 4.0 | 6.0 | 5.0 |
Chemistry | 4.5 | 3.0 | 2.0 | 9.0 |
Data Structures | 3.0 | 5.0 | 8.0 | 7.0 |
Discrete Math | 3.0 | 2.0 | 9.0 | 2.0 |
History | 3.0 | 4.0 | 1.0 | 8.0 |
Literature | 3.0 | 2.0 | 2.0 | 2.0 |
Physics | 4.5 | 6.0 | 5.0 | 7.0 |
Volleyball | 1.5 | 1.0 | 1.0 | 10.0 |
Using pure reinsertion strategy
Pure reinsertion is the default reinsertion strategy, so we should try that one first because we don’t have to change ...