metaheuristic_designer.parameter_schedules.strided_schedule module

Strided schedule that applies a subschedule making the parameter updates in long intervals.

class StridedSchedule(subschedule, iterations=100)

Bases: SchedulableParameter

Schedule that applies a subschedule when a number of iterations have passed, keeping the previous value between updates.

Parameters:

subschedule: SchedulableParameter

Parameter schedule to modify the parameter each iterations iterations.

iterationsint, optional

iterations to keep the current value unchanged, by default 100

Parameters:

• subschedule (SchedulableParameter)

• iterations (int)

Methods

__call__(progress)	Shorthand for evaluate().
evaluate(progress)	Return the parameter value at the given progress.

evaluate(progress)

Return the parameter value at the given progress.

Return type:

float

Parameters:

progressfloat

Current progress, a number between 0 (start) and 1 (end).

Returns:

Any

The parameter value at this stage of the optimization.

Parameters:

progress (float)

Notes

The return value is not restricted to numbers. You can return: * a float (e.g., a linearly decaying mutation strength), * an int (e.g., a discrete number of mutated components), * a bool (e.g., switching on/off a feature after a threshold), * a string (e.g., switching between strategies), or * any other object that the consuming component expects.

This makes schedules suitable for changing discrete algorithm choices as well as continuous numerical parameters.