Evolution Strategies |
This topic contains the following sections:
Evolution strategies use natural problem-dependent representations, and primarily mutation and selection, as search operators. In common with evolutionary algorithms, the operators are applied in a loop. An iteration of the loop is called a generation. The sequence of generations is continued until a termination criterion is met.
This section describes next evolution strategy algorithms:
ImplementationNext most important methods and properties are featured in the BaseOptimizer class.
Optimization start routine:
Method | Description |
|---|---|
Starts objective function minimization. |
Termination conditions properties:
Property | Description |
|---|---|
The maximal optimization iterations number. | |
The algorithm timeout: the execution will be interrupted on the next step after the timeout expiration. | |
The objective change stop criterion. | |
The user's stop criterion function. |
Optimization restart property:
Property | Description |
|---|---|
The minimization process restarts number. |
Optimization solution properties:
Property | Description |
|---|---|
The best found solution point. | |
The best found objective function value. | |
The step number where the best solution found. | |
Total minimization steps number. |
See Also