Moving Peaks

class MovingPeaks(dimensions, **kwargs)

The Moving Peaks Benchmark is a fitness function changing over time.
It consists of a number of peaks changing in height, width and location.
If the kwarg npeaks is a list of three integers, the number of peaks
will fluctuate between the first and the third element of that list, where
the second element is the initial number of peaks. When fluctuating the
number of peaks, the kwarg change_severity must be included in kwargs.
The default configuration of the Moving Peaks benchmark is MPConfigs.DEFAULT.

Parameters

dimensions (int) – The dimensionality of the search domain.
kwargs (Optional) – Keyword arguments, optional.

Table of Kwargs

Parameter	Type	Details
`pfunc`	Callable	The peak function or a list of peak functions.
`bfunc`	Callable	Basis function for static landscape.
`npeaks`	NumOrSeq	Number of peaks. An integer or a list of three integers [min, initial, max].
`change_severity`	float	The fraction of the number of peaks that is allowed to change.
`min_coord`	float	Minimum coordinate for the centre of the peaks.
`max_coord`	float	Maximum coordinate for the centre of the peaks.
`min_height`	float	Minimum height of the peaks.
`max_height`	float	Maximum height of the peaks.
`uniform_height`	float	Starting height of all peaks. Random, if `uniform_height <= 0`.
`min_width`	float	Minimum width of the peaks.
`max_width`	float	Maximum width of the peaks
`uniform_width`	float	Starting width of all peaks. Random, if `uniform_width <= 0`.
`lambda_`	float	Correlation between changes.
`move_severity`	float	The distance a single peak moves when peaks change.
`height_severity`	float	The standard deviation of the change to the height of a peak when peaks change.
`width_severity`	float	The standard deviation of the change to the width of a peak when peaks change.
`period`	int	Period between two changes.

change_peaks(): Changes the position, the height, the width and the number of peaks.

__call__(individual, count=True)

Evaluate the given individual in the context of the current configuration.

Parameters

individual (Individual) – The individual to be evaluated.
count (bool) – Whether to count this evaluation in the total evaluation count, optional.

Returns

The fitness of the individual.

Return type

tuple[float]

property global_maximum: tuple: Returns the value and position of the largest peak.

property sorted_maxima: list: Returns all visible peak values and positions, sorted from the largest to the smallest peaks.

property offline_error: float: Returns the offline error of the landscape.

property current_error: Optional[float]: Returns the current error of the landscape.

class MPConfigs

This class contains the configuration presets for the Moving Peaks problem.

The presets are of type dict and can be accessed as class attributes.

Table of Presets

Keys / Presets

DEFAULT

ALT1

ALT2

pfunc

MPFuncs.pf1()

MPFuncs.pf2()

MPFuncs.pf2()

bfunc

None

None

lambda x: 10

npeaks

5

10

50

change_severity

None

None

None

min_coord

0.0

0.0

0.0

max_coord

100.0

100.0

100.0

min_height

30.0

30.0

30.0

max_height

70.0

70.0

70.0

uniform_height

50.0

50.0

0.0

min_width

0.0001

1.0

1.0

max_width

0.2

12.0

12.0

uniform_width

0.1

0.0

0.0

lambda_

0.0

0.5

0.5

move_severity

1.0

1.5

1.0

height_severity

7.0

7.0

1.0

width_severity

0.01

1.0

0.5

period

5000

5000

1000

class MPFuncs

This class contains the peak functions for the Moving Peaks problem.

These functions can be used for creating custom configuration presets.