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An optimize step generates optimized configurations according to the defined optimization strategy. During this step, Akamas AI is used to generate such optimized configurations.
The optimize step has the following structure:
optimizerfield allows selecting the desired optimizer:
AKAMASidentifies the standard AI optimizer used by Akamas
RANDOMidentifies an optimization that generates configurations using random numbers
Notice that SOBOL and RANDOM optimizers do not perform initialization experiments, hence the field
The optimize step is fault-tolerant and tries to relaunch experiments on failure. Nevertheless, the step imposes a limit on the number of failed experiments: if too many experiments fail, then the entire step fails too. By default, at most 30 experiments can fail while Akamas is optimizing systems. An experiment is considered failed when it either failed to run (i.e., there is an error in the workflow) or violated some constraint.
The optimize step launches some initialization experiments (by default 10) that do not apply the AI optimizer and are used to find good configurations. By default, the step performs 10 initialization experiments.
Initialization experiments take into account bootstrapped experiments, experiments executed in preset steps, and baseline experiments.
The following snippet shows an optimization step that runs 50 experiments using the SOBOL optimizer:
name: "my_optimize" # name of the step
type: "optimize" # type of the step (optimize)
numberOfExperiments: 50 # amount of experiments to execute
numberOfTrials: 2 # amount of trials for each experiment