Hi Daniel,
As per my understanding, you have a 9-input function, which outputs a fitness penalty value, and you would like to optimize the function to get a positive fitness penalty.
Optimization problems, especially with genetic algorithms (GA), can be quite tricky due to their stochastic nature. There can be several reasons why you are not getting a positive fitness penalty. Here are a few suggestions:
- Fitness Function: You mentioned that you give a penalty of -800 or -400 if the solution fails at certain stages. I would suggest you not to return a very large negative value for the fitness function in case of failure as this might discourage the algorithm from exploring those regions of the solution space
- Mutation and Crossover Rates: You could try adjusting the mutation and crossover rates. If the mutation rate is too low, the algorithm might not explore the solution space sufficiently. If it's too high, the algorithm might become a random search. Similarly, the crossover rate controls the extent to which information is shared between individuals.
- Stall Generations: You could also try increasing the number of stall generations. This is the number of generations without any improvement after which the algorithm stops. Increasing this number gives the algorithm more time to find a better solution.
- Constraint Handling: Make sure to handle the problem constraints correctly. Improper constraint handling can lead the algorithm to infeasible solutions.
Kindly refer to the following documentation links to have more information on the following:
- 'ga' function: https://www.mathworks.com/help/gads/ga.html#responsive_offcanvas
- Varying Mutation and Crossover: https://www.mathworks.com/help/gads/vary-mutation-and-crossover.html
- Stall Generations: https://www.mathworks.com/help/gads/setting-the-maximum-number-of-generations.html
Hope that helps!
Balavignesh
