Reading

• Haupt and Haput
  • Chapter 3 on the Continuous GA.
    You should read this in relation to the exercises you did last week. How would you adapt the GA to handle floating point chromosomes?
  • Chapter 4

Briefing

• The Cross-Over function
• The Mutation function

Exercises

As we did last week, we will use the demo on github.
You may want to check for a new release.

The exercises below are phrased more briefly than last week, but this does not mean that you should be as brief in your solutions. The intention is to play around and understand what configurations make a difference to the performance of a GA.

Discussion

What do we need to change to turn the binary GA into a Continuous GA?

Inspect your demo code and discuss if the changes made there are appropriate.

Initial testing

1. Test the Continuous GA using the test script and make sure it works on your system.
2. Test the (continuous) cost functions that you tested last week on the continuous GA.
3. Are there cost functions where the binary GA performs better? Where the Continuous GA performs better?

Varying the GA

Consider alternative mutation and crossover functions for the Continuous GA. Implement a few variations and test if you can improve the performance compared to the default.

New Problems