NiaPy.algorithms.modified.jde.AgingIndividualJDE.__init__() - Code Metrics - Inspection of "Merge pull request #262 from lucijabrezocnik/maste..." - NiaOrg/NiaPy - Measure and Improve Code Quality continuously with Scrutinizer

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AgingIndividualJDE.init() A

↳ Parent: NiaPy.algorithms.modified.jde

Complexity

Conditions

Size

Total Lines	11
Code Lines	3

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	3
nop	2
dl	0
loc	11
rs	10
c	0
b	0
f	0

# encoding=utf8
import logging

from NiaPy.algorithms.algorithm import Individual
from NiaPy.algorithms.basic.de import DifferentialEvolution, CrossBest1, CrossRand1, CrossCurr2Best1, CrossBest2, CrossCurr2Rand1, proportional, multiMutations, DynNpDifferentialEvolution
from NiaPy.util.utility import objects2array

logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.modified')
logger.setLevel('INFO')

__all__ = [
	'SelfAdaptiveDifferentialEvolution',
	'DynNpSelfAdaptiveDifferentialEvolutionAlgorithm',
	'AgingSelfAdaptiveDifferentialEvolution',
	'MultiStrategySelfAdaptiveDifferentialEvolution',
	'DynNpMultiStrategySelfAdaptiveDifferentialEvolution'
]

class SolutionjDE(Individual):
	r"""Individual for jDE algorithm.

	Attributes:
		F (float): Scale factor.
		CR (float): Crossover probability.

	See Also:
		:class:`NiaPy.algorithms.Individual`
	"""
	def __init__(self, F=2, CR=0.5, **kwargs):
		r"""Initialize SolutionjDE.

		Attributes:
			F (float): Scale factor.
			CR (float): Crossover probability.

		See Also:
			:func:`NiaPy.algorithm.Individual.__init__`
		"""
		Individual.__init__(self, **kwargs)
		self.F, self.CR = F, CR

class SelfAdaptiveDifferentialEvolution(DifferentialEvolution):
	r"""Implementation of Self-adaptive differential evolution algorithm.

	Algorithm:
		Self-adaptive differential evolution algorithm

	Date:
		2018

	Author:
		Uros Mlakar and Klemen Berkovič

	License:
		MIT

	Reference paper:
		Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE transactions on evolutionary computation, 10(6), 646-657, 2006.

	Attributes:
		Name (List[str]): List of strings representing algorithm name
		F_l (float): Scaling factor lower limit.
		F_u (float): Scaling factor upper limit.
		Tao1 (float): Change rate for F parameter update.
		Tao2 (float): Change rate for CR parameter update.

	See Also:
		* :class:`NiaPy.algorithms.basic.DifferentialEvolution`
	"""
	Name = ['SelfAdaptiveDifferentialEvolution', 'jDE']

	@staticmethod
	def algorithmInfo():
		r"""Get algorithm information.

		Returns:
			str: Algorithm information.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE transactions on evolutionary computation, 10(6), 646-657, 2006."""

	@staticmethod

	def typeParameters():
		r"""Get dictionary with functions for checking values of parameters.

		Returns:
			Dict[str, Callable]:
				* F_l (Callable[[Union[float, int]], bool])
				* F_u (Callable[[Union[float, int]], bool])
				* Tao1 (Callable[[Union[float, int]], bool])
				* Tao2 (Callable[[Union[float, int]], bool])

		See Also:
			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.typeParameters`
		"""
		d = DifferentialEvolution.typeParameters()
		d['F_l'] = lambda x: isinstance(x, (float, int)) and x > 0
		d['F_u'] = lambda x: isinstance(x, (float, int)) and x > 0
		d['Tao1'] = lambda x: isinstance(x, (float, int)) and 0 <= x <= 1
		d['Tao2'] = lambda x: isinstance(x, (float, int)) and 0 <= x <= 1
		return d

	def setParameters(self, F_l=0.0, F_u=1.0, Tao1=0.4, Tao2=0.2, **ukwargs):
		r"""Set the parameters of an algorithm.

		Arguments:
			F_l (Optional[float]): Scaling factor lower limit.
			F_u (Optional[float]): Scaling factor upper limit.
			Tao1 (Optional[float]): Change rate for F parameter update.
			Tao2 (Optional[float]): Change rate for CR parameter update.

		See Also:
			* :func:`NiaPy.algorithms.basic.DifferentialEvolution.setParameters`
		"""
		DifferentialEvolution.setParameters(self, itype=ukwargs.pop('itype', SolutionjDE), **ukwargs)
		self.F_l, self.F_u, self.Tao1, self.Tao2 = F_l, F_u, Tao1, Tao2

	def getParameters(self):
		r"""TODO.

		Returns:
			Dict[str, Any]: TODO.
		"""
		d = DifferentialEvolution.getParameters(self)
		d.update({
			'F_l': self.F_l,
			'F_u': self.F_u,
			'Tao1': self.Tao1,
			'Tao2': self.Tao2
		})
		return d

	def AdaptiveGen(self, x):
		r"""Adaptive update scale factor in crossover probability.

		Args:
			x (Individual): Individual to apply function on.

		Returns:
			Individual: New individual with new parameters
		"""
		f = self.F_l + self.rand() * (self.F_u - self.F_l) if self.rand() < self.Tao1 else x.F
		cr = self.rand() if self.rand() < self.Tao2 else x.CR
		return self.itype(x=x.x, F=f, CR=cr, e=False)

	def evolve(self, pop, xb, task, **ukwargs):
		r"""Evolve current population.

		Args:
			pop (numpy.ndarray[Individual]): Current population.
			xb (Individual): Global best individual.
			task (Task): Optimization task.
			ukwargs (Dict[str, Any]): Additional arguments.

		Returns:
			numpy.ndarray: New population.
		"""
		npop = objects2array([self.AdaptiveGen(e) for e in pop])
		for i, e in enumerate(npop): npop[i].x = self.CrossMutt(npop, i, xb, e.F, e.CR, rnd=self.Rand)
		for e in npop: e.evaluate(task, rnd=self.rand)
		return npop

class AgingIndividualJDE(SolutionjDE):
	r"""Individual with age.

	Attributes:
		age (int): Age of individual.

	See Also:
		* :func:`NiaPy.algorithms.modified.SolutionjDE`
	"""
	def __init__(self, **kwargs):
		r"""Initialize aging individual for jDE algorithm.

		Args:
			**kwargs (Dict[str, Any]): Additional arguments.

		See Also:
			* :func:`NiaPy.algorithms.modified.SolutionjDE.__init__`
		"""
		SolutionjDE.__init__(self, **kwargs)
		self.age = 0

class AgingSelfAdaptiveDifferentialEvolution(SelfAdaptiveDifferentialEvolution):
	r"""Implementation of Dynamic population size with aging self-adaptive differential evolution algorithm.

	Algorithm:
		Dynamic population size with aging self-adaptive self adaptive differential evolution algorithm

	Date:
		2018

	Author:
		Jan Popič and Klemen Berkovič

	License:
		MIT

	Reference URL:
		https://link.springer.com/article/10.1007/s10489-007-0091-x

	Reference paper:
		Brest, Janez, and Mirjam Sepesy Maučec. Population size reduction for the differential evolution algorithm. Applied Intelligence 29.3 (2008): 228-247.

	Attributes:
		Name (List[str]): List of strings representing algorithm name.
	"""
	Name = ['AgingSelfAdaptiveDifferentialEvolution', 'ANpjDE']

	@staticmethod
	def algorithmInfo():
		r"""Get basic information about the algorithm.

		Returns:
			str: Basic information.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""Brest, Janez, and Mirjam Sepesy Maučec. Population size reduction for the differential evolution algorithm. Applied Intelligence 29.3 (2008): 228-247."""

	@staticmethod
	def typeParameters():
		d = SelfAdaptiveDifferentialEvolution.typeParameters()
		# TODO
		return d

	def setParameters(self, LT_min=1, LT_max=7, age=proportional, **ukwargs):
		r"""Set core parameters of AgingSelfAdaptiveDifferentialEvolution algorithm.

		Args:
			LT_min (Optional[int]): Minimum age.
			LT_max (Optional[int]): Maximum age.
			age (Optional[Callable[[], int]]): Function for calculating age of individual.
			**ukwargs (Dict[str, Any]): Additional arguments.

		See Also:
			* :func:`SelfAdaptiveDifferentialEvolution.setParameters`
		"""
		SelfAdaptiveDifferentialEvolution.setParameters(self, **ukwargs)
		self.LT_min, self.LT_max, self.age = LT_min, LT_max, age
		self.mu = abs(self.LT_max - self.LT_min) / 2

class DynNpSelfAdaptiveDifferentialEvolutionAlgorithm(SelfAdaptiveDifferentialEvolution, DynNpDifferentialEvolution):
	r"""Implementation of Dynamic population size self-adaptive differential evolution algorithm.

	Algorithm:
		Dynamic population size self-adaptive differential evolution algorithm

	Date:
		2018

	Author:
		Jan Popič and Klemen Berkovič

	License:
		MIT

	Reference URL:
		https://link.springer.com/article/10.1007/s10489-007-0091-x

	Reference paper:
		Brest, Janez, and Mirjam Sepesy Maučec. Population size reduction for the differential evolution algorithm. Applied Intelligence 29.3 (2008): 228-247.

	Attributes:
		Name (List[str]): List of strings representing algorithm name.
		rp (int): Small non-negative number which is added to value of generations.
		pmax (int): Number of population reductions.

	See Also:
		* :class:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution`
	"""
	Name = ['DynNpSelfAdaptiveDifferentialEvolutionAlgorithm', 'dynNPjDE']

	@staticmethod
	def algorithmInfo():
		r"""Get basic information about the algorithm.

		Returns:
			str: Basic information.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		return r"""Brest, Janez, and Mirjam Sepesy Maučec. Population size reduction for the differential evolution algorithm. Applied Intelligence 29.3 (2008): 228-247."""

	@staticmethod
	def typeParameters():
		r"""Get basic information of algorithm.

		Returns:
			str: Basic information of algorithm.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
		"""
		d = SelfAdaptiveDifferentialEvolution.typeParameters()
		d['rp'] = lambda x: isinstance(x, (float, int)) and x > 0
		d['pmax'] = lambda x: isinstance(x, int) and x > 0
		return d

	def setParameters(self, rp=0, pmax=10, **ukwargs):
		r"""Set the parameters of an algorithm.

		Arguments:
			rp (Optional[int]): Small non-negative number which is added to value of genp (if it's not divisible).
			pmax (Optional[int]): Number of population reductions.

		See Also:
			* :func:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution.setParameters`
		"""
		DynNpDifferentialEvolution.setParameters(self, rp=rp, pmax=pmax, **ukwargs)
		SelfAdaptiveDifferentialEvolution.setParameters(self, **ukwargs)

	def postSelection(self, pop, task, **kwargs):
		r"""Post selection operator.

		Args:
			pop (numpy.ndarray[Individual]): Current population.
			task (Task): Optimization task.

		Returns:
			numpy.ndarray[Individual]: New population.
		"""
		return DynNpDifferentialEvolution.postSelection(self, pop, task, **kwargs)

class MultiStrategySelfAdaptiveDifferentialEvolution(SelfAdaptiveDifferentialEvolution):
	r"""Implementation of self-adaptive differential evolution algorithm with multiple mutation strategys.

	Algorithm:
		Self-adaptive differential evolution algorithm with multiple mutation strategys

	Date:
		2018

	Author:
		Klemen Berkovič

	License:
		MIT

	Attributes:
		Name (List[str]): List of strings representing algorithm name

	See Also:
		* :class:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution`
	"""
	Name = ['MultiStrategySelfAdaptiveDifferentialEvolution', 'MsjDE']

	def setParameters(self, strategies=(CrossCurr2Rand1, CrossCurr2Best1, CrossRand1, CrossBest1, CrossBest2), **kwargs):
		r"""Set core parameters of MultiStrategySelfAdaptiveDifferentialEvolution algorithm.

		Args:
			strategys (Optional[Iterable[Callable]]): Mutations strategies to use in algorithm.
			**kwargs:

		See Also:
			* :func:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution.setParameters`
		"""
		SelfAdaptiveDifferentialEvolution.setParameters(self, CrossMutt=kwargs.pop('CrossMutt', multiMutations), **kwargs)
		self.strategies = strategies

	def evolve(self, pop, xb, task, **kwargs):

		r"""Evolve population with the help multiple mutation strategies.

		Args:
			pop (numpy.ndarray[Individual]): Current population.
			xb (Individual): Current best individual.
			task (Task): Optimization task.
			**kwargs (Dict[str, Any]): Additional arguments.

		Returns:
			numpy.ndarray[Individual]: New population of individuals.
		"""
		return objects2array([self.CrossMutt(pop, i, xb, self.F, self.CR, self.Rand, task, self.itype, self.strategies) for i in range(len(pop))])

class DynNpMultiStrategySelfAdaptiveDifferentialEvolution(MultiStrategySelfAdaptiveDifferentialEvolution, DynNpSelfAdaptiveDifferentialEvolutionAlgorithm):
	r"""Implementation of Dynamic population size self-adaptive differential evolution algorithm with multiple mutation strategies.

	Algorithm:
		Dynamic population size self-adaptive differential evolution algorithm with multiple mutation strategies

	Date:
		2018

	Author:
		Klemen Berkovič

	License:
		MIT

	Attributes:
		Name (List[str]): List of strings representing algorithm name.

	See Also:
		* :class:`NiaPy.algorithms.modified.MultiStrategySelfAdaptiveDifferentialEvolution`
		* :class:`NiaPy.algorithms.modified.DynNpSelfAdaptiveDifferentialEvolutionAlgorithm`
	"""
	Name = ['DynNpMultiStrategySelfAdaptiveDifferentialEvolution', 'dynNpMsjDE']

	def setParameters(self, pmax=10, rp=5, **kwargs):
		r"""Set core parameters for algorithm instance.

		Args:
			pmax (Optional[int]):
			rp (Optional[int]):
			**kwargs (Dict[str, Any]):

		See Also:
			* :func:`NiaPy.algorithms.modified.MultiStrategySelfAdaptiveDifferentialEvolution.setParameters`
		"""
		MultiStrategySelfAdaptiveDifferentialEvolution.setParameters(self, **kwargs)
		self.pmax, self.rp = pmax, rp

	def postSelection(self, pop, task, **kwargs):
		return DynNpSelfAdaptiveDifferentialEvolutionAlgorithm.postSelection(self, pop, task)

# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3


1		# encoding=utf8
2		import logging
3
4		from NiaPy.algorithms.algorithm import Individual
5		from NiaPy.algorithms.basic.de import DifferentialEvolution, CrossBest1, CrossRand1, CrossCurr2Best1, CrossBest2, CrossCurr2Rand1, proportional, multiMutations, DynNpDifferentialEvolution
6		from NiaPy.util.utility import objects2array
7
8		logging.basicConfig()
9		logger = logging.getLogger('NiaPy.algorithms.modified')
10		logger.setLevel('INFO')
11
12		__all__ = [
13		'SelfAdaptiveDifferentialEvolution',
14		'DynNpSelfAdaptiveDifferentialEvolutionAlgorithm',
15		'AgingSelfAdaptiveDifferentialEvolution',
16		'MultiStrategySelfAdaptiveDifferentialEvolution',
17		'DynNpMultiStrategySelfAdaptiveDifferentialEvolution'
18		]
19
20		class SolutionjDE(Individual):
21		r"""Individual for jDE algorithm.
22
23		Attributes:
24		F (float): Scale factor.
25		CR (float): Crossover probability.
26
27		See Also:
28		:class:`NiaPy.algorithms.Individual`
29		"""
30		def __init__(self, F=2, CR=0.5, **kwargs):
31		r"""Initialize SolutionjDE.
32
33		Attributes:
34		F (float): Scale factor.
35		CR (float): Crossover probability.
36
37		See Also:
38		:func:`NiaPy.algorithm.Individual.__init__`
39		"""
40		Individual.__init__(self, **kwargs)
41		self.F, self.CR = F, CR
42
43		class SelfAdaptiveDifferentialEvolution(DifferentialEvolution):
44		r"""Implementation of Self-adaptive differential evolution algorithm.
45
46		Algorithm:
47		Self-adaptive differential evolution algorithm
48
49		Date:
50		2018
51
52		Author:
53		Uros Mlakar and Klemen Berkovič
54
55		License:
56		MIT
57
58		Reference paper:
59		Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE transactions on evolutionary computation, 10(6), 646-657, 2006.
60
61		Attributes:
62		Name (List[str]): List of strings representing algorithm name
63		F_l (float): Scaling factor lower limit.
64		F_u (float): Scaling factor upper limit.
65		Tao1 (float): Change rate for F parameter update.
66		Tao2 (float): Change rate for CR parameter update.
67
68		See Also:
69		* :class:`NiaPy.algorithms.basic.DifferentialEvolution`
70		"""
71		Name = ['SelfAdaptiveDifferentialEvolution', 'jDE']
72
73		@staticmethod
74		def algorithmInfo():
75		r"""Get algorithm information.
76
77		Returns:
78		str: Algorithm information.
79
80		See Also:
81		* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
82		"""
83		return r"""Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE transactions on evolutionary computation, 10(6), 646-657, 2006."""
84
85	View Code Duplication	@staticmethod
		0 ignored issues – show Duplication introduced 2019-11-10 16:54 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
86		def typeParameters():
87		r"""Get dictionary with functions for checking values of parameters.
88
89		Returns:
90		Dict[str, Callable]:
91		* F_l (Callable[[Union[float, int]], bool])
92		* F_u (Callable[[Union[float, int]], bool])
93		* Tao1 (Callable[[Union[float, int]], bool])
94		* Tao2 (Callable[[Union[float, int]], bool])
95
96		See Also:
97		* :func:`NiaPy.algorithms.basic.DifferentialEvolution.typeParameters`
98		"""
99		d = DifferentialEvolution.typeParameters()
100		d['F_l'] = lambda x: isinstance(x, (float, int)) and x > 0
101		d['F_u'] = lambda x: isinstance(x, (float, int)) and x > 0
102		d['Tao1'] = lambda x: isinstance(x, (float, int)) and 0 <= x <= 1
103		d['Tao2'] = lambda x: isinstance(x, (float, int)) and 0 <= x <= 1
104		return d
105
106		def setParameters(self, F_l=0.0, F_u=1.0, Tao1=0.4, Tao2=0.2, **ukwargs):
107		r"""Set the parameters of an algorithm.
108
109		Arguments:
110		F_l (Optional[float]): Scaling factor lower limit.
111		F_u (Optional[float]): Scaling factor upper limit.
112		Tao1 (Optional[float]): Change rate for F parameter update.
113		Tao2 (Optional[float]): Change rate for CR parameter update.
114
115		See Also:
116		* :func:`NiaPy.algorithms.basic.DifferentialEvolution.setParameters`
117		"""
118		DifferentialEvolution.setParameters(self, itype=ukwargs.pop('itype', SolutionjDE), **ukwargs)
119		self.F_l, self.F_u, self.Tao1, self.Tao2 = F_l, F_u, Tao1, Tao2
120
121		def getParameters(self):
122		r"""TODO.
123
124		Returns:
125		Dict[str, Any]: TODO.
126		"""
127		d = DifferentialEvolution.getParameters(self)
128		d.update({
129		'F_l': self.F_l,
130		'F_u': self.F_u,
131		'Tao1': self.Tao1,
132		'Tao2': self.Tao2
133		})
134		return d
135
136		def AdaptiveGen(self, x):
137		r"""Adaptive update scale factor in crossover probability.
138
139		Args:
140		x (Individual): Individual to apply function on.
141
142		Returns:
143		Individual: New individual with new parameters
144		"""
145		f = self.F_l + self.rand() * (self.F_u - self.F_l) if self.rand() < self.Tao1 else x.F
146		cr = self.rand() if self.rand() < self.Tao2 else x.CR
147		return self.itype(x=x.x, F=f, CR=cr, e=False)
148
149		def evolve(self, pop, xb, task, **ukwargs):
150		r"""Evolve current population.
151
152		Args:
153		pop (numpy.ndarray[Individual]): Current population.
154		xb (Individual): Global best individual.
155		task (Task): Optimization task.
156		ukwargs (Dict[str, Any]): Additional arguments.
157
158		Returns:
159		numpy.ndarray: New population.
160		"""
161		npop = objects2array([self.AdaptiveGen(e) for e in pop])
162		for i, e in enumerate(npop): npop[i].x = self.CrossMutt(npop, i, xb, e.F, e.CR, rnd=self.Rand)
163		for e in npop: e.evaluate(task, rnd=self.rand)
164		return npop
165
166		class AgingIndividualJDE(SolutionjDE):
167		r"""Individual with age.
168
169		Attributes:
170		age (int): Age of individual.
171
172		See Also:
173		* :func:`NiaPy.algorithms.modified.SolutionjDE`
174		"""
175		def __init__(self, **kwargs):
176		r"""Initialize aging individual for jDE algorithm.
177
178		Args:
179		**kwargs (Dict[str, Any]): Additional arguments.
180
181		See Also:
182		* :func:`NiaPy.algorithms.modified.SolutionjDE.__init__`
183		"""
184		SolutionjDE.__init__(self, **kwargs)
185		self.age = 0
186
187		class AgingSelfAdaptiveDifferentialEvolution(SelfAdaptiveDifferentialEvolution):
188		r"""Implementation of Dynamic population size with aging self-adaptive differential evolution algorithm.
189
190		Algorithm:
191		Dynamic population size with aging self-adaptive self adaptive differential evolution algorithm
192
193		Date:
194		2018
195
196		Author:
197		Jan Popič and Klemen Berkovič
198
199		License:
200		MIT
201
202		Reference URL:
203		https://link.springer.com/article/10.1007/s10489-007-0091-x
204
205		Reference paper:
206		Brest, Janez, and Mirjam Sepesy Maučec. Population size reduction for the differential evolution algorithm. Applied Intelligence 29.3 (2008): 228-247.
207
208		Attributes:
209		Name (List[str]): List of strings representing algorithm name.
210		"""
211		Name = ['AgingSelfAdaptiveDifferentialEvolution', 'ANpjDE']
212
213		@staticmethod
214		def algorithmInfo():
215		r"""Get basic information about the algorithm.
216
217		Returns:
218		str: Basic information.
219
220		See Also:
221		* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
222		"""
223		return r"""Brest, Janez, and Mirjam Sepesy Maučec. Population size reduction for the differential evolution algorithm. Applied Intelligence 29.3 (2008): 228-247."""
224
225		@staticmethod
226		def typeParameters():
227		d = SelfAdaptiveDifferentialEvolution.typeParameters()
228		# TODO
229		return d
230
231		def setParameters(self, LT_min=1, LT_max=7, age=proportional, **ukwargs):
232		r"""Set core parameters of AgingSelfAdaptiveDifferentialEvolution algorithm.
233
234		Args:
235		LT_min (Optional[int]): Minimum age.
236		LT_max (Optional[int]): Maximum age.
237		age (Optional[Callable[[], int]]): Function for calculating age of individual.
238		**ukwargs (Dict[str, Any]): Additional arguments.
239
240		See Also:
241		* :func:`SelfAdaptiveDifferentialEvolution.setParameters`
242		"""
243		SelfAdaptiveDifferentialEvolution.setParameters(self, **ukwargs)
244		self.LT_min, self.LT_max, self.age = LT_min, LT_max, age
245		self.mu = abs(self.LT_max - self.LT_min) / 2
246
247		class DynNpSelfAdaptiveDifferentialEvolutionAlgorithm(SelfAdaptiveDifferentialEvolution, DynNpDifferentialEvolution):
248		r"""Implementation of Dynamic population size self-adaptive differential evolution algorithm.
249
250		Algorithm:
251		Dynamic population size self-adaptive differential evolution algorithm
252
253		Date:
254		2018
255
256		Author:
257		Jan Popič and Klemen Berkovič
258
259		License:
260		MIT
261
262		Reference URL:
263		https://link.springer.com/article/10.1007/s10489-007-0091-x
264
265		Reference paper:
266		Brest, Janez, and Mirjam Sepesy Maučec. Population size reduction for the differential evolution algorithm. Applied Intelligence 29.3 (2008): 228-247.
267
268		Attributes:
269		Name (List[str]): List of strings representing algorithm name.
270		rp (int): Small non-negative number which is added to value of generations.
271		pmax (int): Number of population reductions.
272
273		See Also:
274		* :class:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution`
275		"""
276		Name = ['DynNpSelfAdaptiveDifferentialEvolutionAlgorithm', 'dynNPjDE']
277
278		@staticmethod
279		def algorithmInfo():
280		r"""Get basic information about the algorithm.
281
282		Returns:
283		str: Basic information.
284
285		See Also:
286		* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
287		"""
288		return r"""Brest, Janez, and Mirjam Sepesy Maučec. Population size reduction for the differential evolution algorithm. Applied Intelligence 29.3 (2008): 228-247."""
289
290		@staticmethod
291		def typeParameters():
292		r"""Get basic information of algorithm.
293
294		Returns:
295		str: Basic information of algorithm.
296
297		See Also:
298		* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
299		"""
300		d = SelfAdaptiveDifferentialEvolution.typeParameters()
301		d['rp'] = lambda x: isinstance(x, (float, int)) and x > 0
302		d['pmax'] = lambda x: isinstance(x, int) and x > 0
303		return d
304
305		def setParameters(self, rp=0, pmax=10, **ukwargs):
306		r"""Set the parameters of an algorithm.
307
308		Arguments:
309		rp (Optional[int]): Small non-negative number which is added to value of genp (if it's not divisible).
310		pmax (Optional[int]): Number of population reductions.
311
312		See Also:
313		* :func:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution.setParameters`
314		"""
315		DynNpDifferentialEvolution.setParameters(self, rp=rp, pmax=pmax, **ukwargs)
316		SelfAdaptiveDifferentialEvolution.setParameters(self, **ukwargs)
317
318		def postSelection(self, pop, task, **kwargs):
319		r"""Post selection operator.
320
321		Args:
322		pop (numpy.ndarray[Individual]): Current population.
323		task (Task): Optimization task.
324
325		Returns:
326		numpy.ndarray[Individual]: New population.
327		"""
328		return DynNpDifferentialEvolution.postSelection(self, pop, task, **kwargs)
329
330		class MultiStrategySelfAdaptiveDifferentialEvolution(SelfAdaptiveDifferentialEvolution):
331		r"""Implementation of self-adaptive differential evolution algorithm with multiple mutation strategys.
332
333		Algorithm:
334		Self-adaptive differential evolution algorithm with multiple mutation strategys
335
336		Date:
337		2018
338
339		Author:
340		Klemen Berkovič
341
342		License:
343		MIT
344
345		Attributes:
346		Name (List[str]): List of strings representing algorithm name
347
348		See Also:
349		* :class:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution`
350		"""
351		Name = ['MultiStrategySelfAdaptiveDifferentialEvolution', 'MsjDE']
352
353		def setParameters(self, strategies=(CrossCurr2Rand1, CrossCurr2Best1, CrossRand1, CrossBest1, CrossBest2), **kwargs):
354		r"""Set core parameters of MultiStrategySelfAdaptiveDifferentialEvolution algorithm.
355
356		Args:
357		strategys (Optional[Iterable[Callable]]): Mutations strategies to use in algorithm.
358		**kwargs:
359
360		See Also:
361		* :func:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution.setParameters`
362		"""
363		SelfAdaptiveDifferentialEvolution.setParameters(self, CrossMutt=kwargs.pop('CrossMutt', multiMutations), **kwargs)
364		self.strategies = strategies
365
366	View Code Duplication	def evolve(self, pop, xb, task, **kwargs):
		0 ignored issues – show Duplication introduced 2019-04-15 10:29 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
367		r"""Evolve population with the help multiple mutation strategies.
368
369		Args:
370		pop (numpy.ndarray[Individual]): Current population.
371		xb (Individual): Current best individual.
372		task (Task): Optimization task.
373		**kwargs (Dict[str, Any]): Additional arguments.
374
375		Returns:
376		numpy.ndarray[Individual]: New population of individuals.
377		"""
378		return objects2array([self.CrossMutt(pop, i, xb, self.F, self.CR, self.Rand, task, self.itype, self.strategies) for i in range(len(pop))])
379
380		class DynNpMultiStrategySelfAdaptiveDifferentialEvolution(MultiStrategySelfAdaptiveDifferentialEvolution, DynNpSelfAdaptiveDifferentialEvolutionAlgorithm):
381		r"""Implementation of Dynamic population size self-adaptive differential evolution algorithm with multiple mutation strategies.
382
383		Algorithm:
384		Dynamic population size self-adaptive differential evolution algorithm with multiple mutation strategies
385
386		Date:
387		2018
388
389		Author:
390		Klemen Berkovič
391
392		License:
393		MIT
394
395		Attributes:
396		Name (List[str]): List of strings representing algorithm name.
397
398		See Also:
399		* :class:`NiaPy.algorithms.modified.MultiStrategySelfAdaptiveDifferentialEvolution`
400		* :class:`NiaPy.algorithms.modified.DynNpSelfAdaptiveDifferentialEvolutionAlgorithm`
401		"""
402		Name = ['DynNpMultiStrategySelfAdaptiveDifferentialEvolution', 'dynNpMsjDE']
403
404		def setParameters(self, pmax=10, rp=5, **kwargs):
405		r"""Set core parameters for algorithm instance.
406
407		Args:
408		pmax (Optional[int]):
409		rp (Optional[int]):
410		**kwargs (Dict[str, Any]):
411
412		See Also:
413		* :func:`NiaPy.algorithms.modified.MultiStrategySelfAdaptiveDifferentialEvolution.setParameters`
414		"""
415		MultiStrategySelfAdaptiveDifferentialEvolution.setParameters(self, **kwargs)
416		self.pmax, self.rp = pmax, rp
417
418		def postSelection(self, pop, task, **kwargs):
419		return DynNpSelfAdaptiveDifferentialEvolutionAlgorithm.postSelection(self, pop, task)
420
421		# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
422

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