def __init__(self, **kwargs):
r"""Initialize algorithm and create name for an algorithm.
**Arguments**:
name {string} -- Full name of algorithm
shortName {string} -- Short name of algorithm
NP {integer} -- population size
D {integer} -- dimension of problem
nGEN {integer} -- nuber of generation/iterations
nFES {integer} -- number of function evaluations
benchmark {object} -- benchmark implementation object
task {Task} -- task to perform optimization on
**See**:
Algorithm.setParameters(self, **kwargs)
"""
task = kwargs.get('task', None)
self.name, self.sName, self.rand = kwargs.get(
'name',
'Algorith'), kwargs.get('sName',
'algo'), RandomState(kwargs.get('seed', 1))
self.task = task if task != None else Task(
kwargs.get('D', 10), kwargs.get('nFES', 100000), None,
kwargs.get('benchmark', 'ackley'))
self.setParameters(**kwargs)
def setUp(self):
self.D = 20
self.x, self.task = rnd.uniform(-2, 2,
self.D), Task(self.D, 230, None,
MyBenchmark())
self.sol1, self.sol2, self.sol3 = MkeSolution(x=self.x), MkeSolution(
task=self.task), MkeSolution(x=self.x)
def setUp(self):
self.D, self.F, self.CR = 10, 0.9, 0.3
self.x, self.task = rnd.uniform(10, 50,
self.D), Task(self.D, 230, None,
MyBenchmark())
self.s1, self.s2 = SolutionjDE(task=self.task), SolutionjDE(x=self.x,
CR=self.CR,
F=self.F)
def __init__(self, **kwargs):
r"""Initialize algorithm and create name for an algorithm.
**Arguments:**
name {string} -- Full name of algorithm
shortName {string} -- Short name of algorithm
NP {integer} -- population size
D {integer} -- dimension of problem
nGEN {integer} -- nuber of generation/iterations
nFES {integer} -- number of function evaluations
benchmark {object} -- benchmark implementation object
task {Task} -- task to perform optimization on
**Raises:**
TypeError -- Raised when given benchmark function which does not exists.
**See**:
Algorithm.setParameters(self, **kwargs)
"""
task, self.name, self.sName, self.Rand = kwargs.pop(
'task', None), kwargs.pop('name', 'Algorith'), kwargs.pop(
'sName', 'algo'), rand.RandomState(kwargs.pop('seed', 1))
self.task = task if task != None else Task(
kwargs.pop('D', 10),
kwargs.pop('nFES', 100000),
kwargs.pop('nGEN', None),
kwargs.pop('benchmark', 'ackley'),
optType=kwargs.pop('optType', OptimizationType.MINIMIZATION))
self.setParameters(**kwargs)
def setUp(self):
self.D, self.nFES, self.nGEN = 10, 10, 10
self.t = Task(self.D, self.nFES, self.nGEN, MyBenchmark())
def setUp(self): self.D = 20 self.x, self.task = rnd.uniform(-100, 100, self.D), Task(self.D, 230, None, MyBenchmark()) self.s1, self.s2, self.s3 = Individual(x=self.x), Individual(task=self.task, rand=rnd), Individual(task=self.task)