Model¶
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class
pymoo.core.algorithm.Algorithm(termination=None, output=None, display=None, callback=None, archive=None, return_least_infeasible=False, save_history=False, verbose=False, seed=None, evaluator=None, **kwargs)¶ - Attributes
- n_gen
Methods
advance
ask
finalize
has_next
infill
next
result
run
setup
tell
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class
pymoo.core.sampling.Sampling¶ This abstract class represents any sampling strategy that can be used to create an initial population or an initial search point.
Methods
do(problem, n_samples, **kwargs)Sample new points with problem information if necessary.
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class
pymoo.core.selection.Selection(**kwargs)¶ This class is used to select parents for the mating or other evolutionary operators. Several strategies can be used to increase the selection pressure.
Methods
do(problem, pop, n_select, n_parents[, to_pop])Choose from the population new individuals to be selected.
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do(problem, pop, n_select, n_parents, to_pop=True, **kwargs)¶ Choose from the population new individuals to be selected.
- Parameters
- problem: class
The problem to be solved. Provides information such as lower and upper bounds or feasibility conditions for custom crossovers.
- pop
Population The population which should be selected from. Some criteria from the design or objective space might be used for the selection. Therefore, only the number of individual might be not enough.
- n_selectint
Number of individuals to select.
- n_parentsint
Number of parents needed to create an offspring.
- to_popbool
Whether IF(!) the implementation returns only indices, it should be converted to individuals.
- Returns
- parentslist
List of parents to be used in the crossover
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class
pymoo.core.mutation.Mutation(prob=1.0, prob_var=None, **kwargs)¶ Methods
do
get_prob_var
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class
pymoo.core.crossover.Crossover(n_parents, n_offsprings, prob=0.9, **kwargs)¶ Methods
do
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class
pymoo.core.survival.Survival(filter_infeasible=True)¶ Methods
do
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class
pymoo.core.termination.Termination¶ Methods
update(algorithm)Provide the termination criterion a current status of the algorithm to update the perc.
do_continue
has_terminated
terminate
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update(algorithm)¶ Provide the termination criterion a current status of the algorithm to update the perc.
- Parameters
- algorithmobject
The algorithm object which is used to determine whether a run has terminated.
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class
pymoo.core.indicator.Indicator(**kwargs)¶ Methods
__call__(F, *args, **kwargs)Call self as a function.
do
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class
pymoo.core.population.Population(individuals=[])¶ Methods
apply
collect
create
empty
get
has
merge
new
set
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class
pymoo.core.individual.Individual(config=None, **kwargs)¶ - Attributes
- CV
- F
- FEAS
- G
- H
- X
- cv
- dF
- dG
- dH
- ddF
- ddG
- ddH
- f
- feas
- feasible
- x
Methods
copy
default_config
duplicate
get
has
new
reset
set
set_by_dict
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class
pymoo.core.result.Result¶ The resulting object of an optimization run.
- Attributes
- cv
- f
- feas