Details
The penalty computes like sum(w[i] * (x[i]-xfeas[i])**2),
where xfeas is the closest feasible (in-bounds) solution from x.
The weight w[i] should be updated during each iteration using
the update method.
Example:
>>> import cma
>>> cma.fmin(cma.felli, 6 * [1], 1,
... {
... 'boundary_handling': 'BoundPenalty',
... 'bounds': [-1, 1],
... 'fixed_variables': {0: 0.012, 2:0.234}
... })
Reference: Hansen et al 2009, A Method for Handling Uncertainty...
IEEE TEC, with addendum, see
http://www.lri.fr/~hansen/TEC2009online.pdf
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__init__(self,
bounds=None)
Argument bounds can be None or bounds[0] and bounds[1]
are lower and upper domain boundaries, each is either None or
a scalar or a list or array of appropriate size. |
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repair(self,
x,
copy_if_changed=True,
copy_always=False)
sets out-of-bounds components of x on the bounds. |
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__call__(self,
x,
archive,
gp)
returns the boundary violation penalty for x ,where x is a
single solution or a list or array of solutions. |
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feasible_ratio(self,
solutions)
counts for each coordinate the number of feasible values in
solutions and returns an array of length len(solutions[0])
with the ratios. |
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update(self,
function_values,
es)
updates the weights for computing a boundary penalty. |
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Inherited from BoundaryHandlerBase:
get_bounds,
has_bounds,
inverse,
is_in_bounds,
to_dim_times_two
Inherited from object:
__delattr__,
__format__,
__getattribute__,
__hash__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
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