Configuration #

bool isCachingSamples# (global)

• When set to true, the objective value will only be calculated once per parameter.
• The objective value of recurring parameters are than retrieved from the cache, instead of recalculating them.
• Setting this to true is useful if an optimisation algorithms significantly often revisits previous parameters due to its internal procedure, or because of a large minimal parameter distance.
• The number of revisits can be measured by the difference between the number of evaluations and the number of distinct evaluations.
• The default value is false.
Number of evaluations: 3
Number of discrete evaluations: 2
Number of cached samples: 2
Number of evaluations: 3
Number of discrete evaluations: 3
Number of cached samples: 0


bool isRecordingSampling# (global)

• When set to true, the evaluated parameters and objective value pairs of an optimisation process are recorded (in order of occurrence, including duplicates).
• The recording is cleared when a new optimisation is issued.
• The default value is false.
Sample Nr. | Objective value | Parameter
1 |         -64.509 |    0.7831   0.7984
2 |        -72.4945 |    0.9116   0.1976
3 |        -47.6595 |    0.3352   0.7682
4 |        -52.8372 |    0.2778   0.5540
5 |        -65.9509 |    0.4774   0.6289
6 |        -61.6414 |    0.3648   0.5134
7 |         -50.869 |    0.9522   0.9162
8 |        -68.2186 |    0.6357   0.7173
9 |        -35.7715 |    0.1416   0.6070
10 |        -19.0923 |    0.0163   0.2429


bool isVerbose# (global)

• When set to true, the optimisation process prints information on the problem and notable progress to the standard output stream std::cout.
• The default value is false.
================================================================================
Solving optimisation problem: BBOB Sphere Function (f1)
Number of dimensions: 2
Lower bounds:   -5.0000  -5.0000
Upper bounds:    5.0000   5.0000
Acceptable objective value: -inf
--------------------------------------------------------------------------------
Optimisation strategy: Hooke-Jeeves algorithm
Boundaries handling function: Map to bound
Stagnation detection function: Always false
Restarting function: Random

Iteration #0 (after 47ms) : Found better solution.
Difference to the previous best objective value: -inf
Best objective value: -64.509
Best parameter:    0.7831   0.7984

Iteration #2 (after 417ms) : Found better solution.
Difference to the previous best objective value: -13.3
Best objective value: -77.809
Best parameter:    0.7831   0.2984

Iteration #5 (after 2900ms) : Found better solution.
Difference to the previous best objective value: -1.3625
Best objective value: -79.1715
Best parameter:    0.7831   0.4234

Iteration #9 (after 4379ms) : Found better solution.
Difference to the previous best objective value: -0.010021
Best objective value: -79.1815
Best parameter:    0.7675   0.4234

Terminated (run out of time or iterations).
Took 4379 / 1000000 microseconds
Took 10 / 10 iterations
Difference to the acceptable objective value: inf
Best objective value: -79.1815
Best parameter:    0.7675   0.4234