I run a nested or multi level optimization problem, because the nonlinearity is simply too much for the evolutionary algorithm.
I use optimization to search an initial solution, to further search and interrogate at the second level.
It then essentially helps to cut up the solution space, to help the algorithm.
yet, In a sense, at the 2nd level of optimization, the evolutionary algorithm still descents too quickly. It then causes it to miss the better solution.
I have seen something similar with ordinary nonlinear programming/ optimization based on 2nd order partial derivatives, and newtons step etc.
Is there a way to prevent the evolutionary algorithm from descending too quickly?
It can rather descent slower and recalculate at that point.
What are the implications when this phenomenon is possible with nonlinear optimization and evolutionary algorithms? Still too much/ fine nonlinearity?
If I understand correctly, you are facing the "exploration vs exploitation" problem. In essence you should experiment with the parameters that affect "exploration". Parameters depend on the algorithm you use. For example you could try with (listed in no particular order)
- representation
- population size (increase)
- mutation operators
- crossover operators
- selection operators
...
Probably you should run a lot of experiments to find the set of parameters that suits you. Also, you should be aware that none of the metaheuristic algorithms in practice (limited execution time) guarantees convergence to the optimal solution. However, with the proper parameters you might get the solutions that are good enough.
I hope that this helps!
If I understand correctly, you are facing the "exploration vs exploitation" problem. In essence you should experiment with the parameters that affect "exploration". Parameters depend on the algorithm you use. For example you could try with (listed in no particular order)
- representation
- population size (increase)
- mutation operators
- crossover operators
- selection operators
...
Probably you should run a lot of experiments to find the set of parameters that suits you. Also, you should be aware that none of the metaheuristic algorithms in practice (limited execution time) guarantees convergence to the optimal solution. However, with the proper parameters you might get the solutions that are good enough.
I hope that this helps!
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