Model¶

class
pymoo.core.algorithm.
Algorithm
(**kwargs)¶ This class represents the abstract class for any algorithm to be implemented. Most importantly it provides the solve method that is used to optimize a given problem.
The solve method provides a wrapper function which does validate the input.
 Parameters
 problem
Problem
Problem to be solved by the algorithm
 termination: :class:`~pymoo.core.termination.Termination`
Object that tells the algorithm when to terminate.
 seedint
Random seed to be used. Same seed is supposed to return the same result. If set to None, a random seed is chosen randomly and stored in the result object to ensure reproducibility.
 verbosebool
If true information during the algorithm execution are displayed
 callbackfunc
A callback function can be passed that is executed every generation. The parameters for the function are the algorithm itself, the number of evaluations so far and the current population.
 def callback(algorithm):
pass
 save_historybool
If true, a current snapshot of each generation is saved.
 pfnumpy.array
The Paretofront for the given problem. If provided performance metrics are printed during execution.
 return_least_infeasiblebool
Whether the algorithm should return the least infeasible solution, if no solution was found.
 evaluator
Evaluator
The evaluator which can be used to make modifications before calling the evaluate function of a problem.
 problem
Methods
advance
ask
finalize
has_next
infill
next
result
run
setup
tell

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[, pop])Sample new points with problem information if necessary.

do
(problem, n_samples, pop=Population([], dtype=object), **kwargs)¶ Sample new points with problem information if necessary.
 Parameters
 problem
Problem
The problem to which points should be sampled. (lower and upper bounds, discrete, binary, …)
 n_samplesint
Number of samples
 pop
Population
The sampling results are stored in a population. The template of the population can be changed. If ‘none’ simply a numpy array is returned.
 problem
 Returns
 Xnumpy.array
Samples points in a two dimensional array


class
pymoo.core.selection.
Selection
¶ 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
(pop, n_select[, n_parents])Choose from the population new individuals to be selected.

do
(pop, n_select, n_parents=2, **kwargs)¶ Choose from the population new individuals to be selected.
 Parameters
 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.
 pop
 Returns
 Inumpy.array
Indices of selected individuals.


class
pymoo.core.mutation.
Mutation
¶ Methods
do
(problem, pop, **kwargs)Mutate variables in a genetic way.

do
(problem, pop, **kwargs)¶ Mutate variables in a genetic way.
 Parameters
 problemclass
The problem instance  specific information such as variable bounds might be needed.
 popPopulation
A population object
 Returns
 XpPopulation
The mutated population.


class
pymoo.core.crossover.
Crossover
(n_parents, n_offsprings, prob=0.9)¶ The crossover combines parents to offsprings. Some crossover are problem specific and use additional information. This class must be inherited from to provide a crossover method to an algorithm.
Methods
do
(problem, pop, parents, **kwargs)This method executes the crossover on the parents.

do
(problem, pop, parents, **kwargs)¶ This method executes the crossover on the parents. This class wraps the implementation of the class that implements the crossover.
 Parameters
 problem: class
The problem to be solved. Provides information such as lower and upper bounds or feasibility conditions for custom crossovers.
 popPopulation
The population as an object
 parents: numpy.array
The select parents of the population for the crossover
 kwargsdict
Any additional data that might be necessary to perform the crossover. E.g. constants of an algorithm.
 Returns
 offspringsPopulation
The off as a matrix. n_children rows and the number of columns is equal to the variable length of the problem.


class
pymoo.core.survival.
Survival
(filter_infeasible=True)¶ Methods
do

class
pymoo.core.termination.
Termination
¶ Base class for the implementation of a termination criterion for an algorithm.
Methods
do_continue
(algorithm)Whenever the algorithm objects wants to know whether it should continue or not it simply asks the termination criterion for it.
has_terminated
(algorithm)Instead of asking if the algorithm should continue it can also ask if it has terminated.

do_continue
(algorithm)¶ Whenever the algorithm objects wants to know whether it should continue or not it simply asks the termination criterion for it.
 Parameters
 algorithmclass
The algorithm object that is asking if it has terminated or not.
 Returns
 do_continuebool
Whether the algorithm has terminated or not.

has_terminated
(algorithm)¶ Instead of asking if the algorithm should continue it can also ask if it has terminated. (just negates the continue method.)


class
pymoo.core.indicator.
Indicator
(**kwargs)¶ Methods
do

class
pymoo.core.population.
Population
(n_individuals=0)¶ Methods
copy
([order])Return a copy of the array.
collect
create
get
has
merge
new
set

copy
(order='C')¶ Return a copy of the array.
 Parameters
 order{‘C’, ‘F’, ‘A’, ‘K’}, optional
Controls the memory layout of the copy. ‘C’ means Corder, ‘F’ means Forder, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. (Note that this function and
numpy.copy()
are very similar but have different default values for their order= arguments, and this function always passes subclasses through.)
See also
numpy.copy
Similar function with different default behavior
numpy.copyto
Notes
This function is the preferred method for creating an array copy. The function
numpy.copy()
is similar, but it defaults to using order ‘K’, and will not pass subclasses through by default.Examples
>>> x = np.array([[1,2,3],[4,5,6]], order='F')
>>> y = x.copy()
>>> x.fill(0)
>>> x array([[0, 0, 0], [0, 0, 0]])
>>> y array([[1, 2, 3], [4, 5, 6]])
>>> y.flags['C_CONTIGUOUS'] True


class
pymoo.core.individual.
Individual
(X=None, F=None, G=None, dF=None, dG=None, ddF=None, ddG=None, CV=None, feasible=None, **kwargs)¶ Methods
copy
get
has
set
set_by_dict

class
pymoo.core.result.
Result
¶ The resulting object of an optimization run.