desdeo_emo.EAs
This module contains classes implementing different Evolutionary algorithms.
Submodules
Package Contents
Classes
The Base class for decomposition based EAs. 

The python version reference vector guided evolutionary algorithm. 

Python Implementation of NSGAIII. Based on the pymoo package. 

The Base class for decomposition based EAs. 

The python version reference vector guided evolutionary algorithm. 

Python implementation of MOEA/D 

Python Implementation of IBEA. 
 class desdeo_emo.EAs.BaseEA(a_priori: bool = False, interact: bool = False, selection_operator: Type[desdeo_emo.selection.SelectionBase.SelectionBase] = None, n_iterations: int = 10, n_gen_per_iter: int = 100, total_function_evaluations: int = 0, use_surrogates: bool = False, keep_archive: bool = False, save_non_dominated: bool = False)
This class provides the basic structure for Evolutionary algorithms.
 start()
Mimics the structure of the mcdm methods. Returns the request objects from self.retuests().
 end()
To be run at the end of the evolution process.
 _next_gen()
Run one generation of an EA. Change nothing about the parameters.
 iterate(preference=None) Tuple
Run one iteration of EA.
One iteration consists of a constant or variable number of generations. This method leaves EA.params unchanged, except the current iteration count and gen count.
 pre_iteration()
Run this code before every iteration.
 post_iteration()
Run this code after every iteration.
 continue_iteration()
Checks whether the current iteration should be continued or not.
 continue_evolution() bool
Checks whether the current iteration should be continued or not.
 check_FE_count() bool
 Checks whether termination criteria via function evaluation count has been
met or not.
 Returns:
True is function evaluation count limit NOT met.
 Return type:
bool
 set_interaction_type(interaction_type: None  str) None  str
 manage_preferences(preference=None)
Forward the preference to the correct preference handling method.
 Parameters:
preference (_type_, optional) – _description_. Defaults to None.
 Raises:
eaError – Preference handling not implemented.
 requests() Tuple
 non_dominated_archive(individuals: numpy.ndarray, results: desdeo_problem.EvaluationResults) None
 class desdeo_emo.EAs.BaseDecompositionEA(problem: desdeo_problem.MOProblem = None, initial_population: desdeo_emo.population.Population.Population = None, population_size: int = None, population_params: Dict = None, lattice_resolution: int = None, interact: bool = False, n_iterations: int = 10, n_gen_per_iter: int = 100, use_surrogates: bool = False, total_function_evaluations: int = 0, keep_archive: bool = False, save_non_dominated: bool = False)[source]
Bases:
BaseEA
The Base class for decomposition based EAs.
This class contains most of the code to set up the parameters and operators. It also contains the logic of a simple decomposition EA.
 Parameters:
problem (MOProblem) – The problem class object specifying the details of the problem.
selection_operator (Type[SelectionBase], optional) – The selection operator to be used by the EA, by default None.
population_size (int, optional) – The desired population size, by default None, which sets up a default value of population size depending upon the dimensionaly of the problem.
population_params (Dict, optional) – The parameters for the population class, by default None. See desdeo_emo.population.Population for more details.
initial_population (Population, optional) – An initial population class, by default None. Use this if you want to set up a specific starting population, such as when the output of one EA is to be used as the input of another.
lattice_resolution (int, optional) – The number of divisions along individual axes in the objective space to be used while creating the reference vector lattice by the simplex lattice design. By default None
a_priori (bool, optional) – A bool variable defining whether a priori preference is to be used or not. By default False
interact (bool, optional) – A bool variable defining whether interactive preference is to be used or not. By default False
n_iterations (int, optional) – The total number of iterations to be run, by default 10. This is not a hard limit and is only used for an internal counter.
n_gen_per_iter (int, optional) – The total number of generations in an iteration to be run, by default 100. This is not a hard limit and is only used for an internal counter.
total_function_evaluations (int, optional) – Set an upper limit to the total number of function evaluations. When set to zero, this argument is ignored and other termination criteria are used.
 _next_gen()
Run one generation of decomposition based EA. Intended to be used by next_iteration.
 pre_iteration()
Run this code before every iteration.
 _select() list
Describe a selection mechanism. Return indices of selected individuals.
 Returns:
List of indices of individuals to be selected.
 Return type:
list
 request_plot() desdeo_tools.interaction.SimplePlotRequest
 request_preferences() Type[desdeo_tools.interaction.request.BaseRequest]
 requests() Tuple
 end()
Conducts nondominated sorting at the end of the evolution process
 Returns:
 The first element is a 2D array of the decision vectors of the nondominated solutions.
The second element is a 2D array of the corresponding objective values.
 Return type:
tuple
 class desdeo_emo.EAs.RVEA(problem: desdeo_problem.MOProblem, population_size: int = None, population_params: Dict = None, initial_population: desdeo_emo.population.Population.Population = None, alpha: float = 2, lattice_resolution: int = None, selection_type: str = None, interact: bool = False, use_surrogates: bool = False, n_iterations: int = 10, n_gen_per_iter: int = 100, total_function_evaluations: int = 0, time_penalty_component: str  float = None, keep_archive: bool = False, save_non_dominated: bool = False)[source]
Bases:
desdeo_emo.EAs.BaseEA.BaseDecompositionEA
The python version reference vector guided evolutionary algorithm.
Most of the relevant code is contained in the super class. This class just assigns the APD selection operator to BaseDecompositionEA.
NOTE: The APD function had to be slightly modified to accomodate for the fact that this version of the algorithm is interactive, and does not have a set termination criteria. There is a time component in the APD penalty function formula of the type: (t/t_max)^alpha. As there is no set t_max, the formula has been changed. See below, the documentation for the argument: penalty_time_component
See the details of RVEA in the following paper
R. Cheng, Y. Jin, M. Olhofer and B. Sendhoff, A Reference Vector Guided Evolutionary Algorithm for Manyobjective Optimization, IEEE Transactions on Evolutionary Computation, 2016
 Parameters:
problem (MOProblem) – The problem class object specifying the details of the problem.
population_size (int, optional) – The desired population size, by default None, which sets up a default value of population size depending upon the dimensionaly of the problem.
population_params (Dict, optional) – The parameters for the population class, by default None. See desdeo_emo.population.Population for more details.
initial_population (Population, optional) – An initial population class, by default None. Use this if you want to set up a specific starting population, such as when the output of one EA is to be used as the input of another.
alpha (float, optional) – The alpha parameter in the APD selection mechanism. Read paper for details.
lattice_resolution (int, optional) – The number of divisions along individual axes in the objective space to be used while creating the reference vector lattice by the simplex lattice design. By default None
selection_type (str, optional) – One of [“mean”, “optimistic”, “robust”]. To be used in datadriven optimization. To be used only with surrogate models which return an “uncertainity” factor. Using “mean” is equivalent to using the mean predicted values from the surrogate models and is the default case. Using “optimistic” results in using (mean  uncertainity) values from the the surrogate models as the predicted value (in case of minimization). It is (mean + uncertainity for maximization). Using “robust” is the opposite of using “optimistic”.
a_priori (bool, optional) – A bool variable defining whether a priori preference is to be used or not. By default False
interact (bool, optional) – A bool variable defining whether interactive preference is to be used or not. By default False
n_iterations (int, optional) – The total number of iterations to be run, by default 10. This is not a hard limit and is only used for an internal counter.
n_gen_per_iter (int, optional) – The total number of generations in an iteration to be run, by default 100. This is not a hard limit and is only used for an internal counter.
total_function_evaluations (int, optional) – Set an upper limit to the total number of function evaluations. When set to zero, this argument is ignored and other termination criteria are used.
penalty_time_component (Union[str, float], optional) – The APD formula had to be slightly changed. If penalty_time_component is a float between [0, 1], (t/t_max) is replaced by that constant for the entire algorithm. If penalty_time_component is “original”, the original intent of the paper is followed and (t/t_max) is calculated as (current generation count/total number of generations). If penalty_time_component is “function_count”, (t/t_max) is calculated as (current function evaluation count/total number of function evaluations) If penalty_time_component is “interactive”, (t/t_max) is calculated as (Current gen count within an iteration/Total gen count within an iteration). Hence, time penalty is always zero at the beginning of each iteration, and one at the end of each iteration. Note: If the penalty_time_component ever exceeds one, the value one is used as the penalty_time_component. If no value is provided, an appropriate default is selected. If interact is true, penalty_time_component is “interactive” by default. If interact is false, but total_function_evaluations is provided, penalty_time_component is “function_count” by default. If interact is false, but total_function_evaluations is not provided, penalty_time_component is “original” by default.
 _time_penalty_constant()
Returns the constant time penalty value.
 _time_penalty_original()
Calculates the appropriate time penalty value, by the original formula.
 _time_penalty_interactive()
Calculates the appropriate time penalty value.
 _time_penalty_function_count()
Calculates the appropriate time penalty value.
 class desdeo_emo.EAs.NSGAIII(problem: desdeo_problem.MOProblem, population_size: int = None, population_params: Dict = None, n_survive: int = None, initial_population: desdeo_emo.population.Population.Population = None, lattice_resolution: int = None, selection_type: str = None, interact: bool = False, use_surrogates: bool = False, n_iterations: int = 10, n_gen_per_iter: int = 100, total_function_evaluations: int = 0, keep_archive: bool = False, save_non_dominated: bool = False)[source]
Bases:
desdeo_emo.EAs.BaseEA.BaseDecompositionEA
Python Implementation of NSGAIII. Based on the pymoo package.
Most of the relevant code is contained in the super class. This class just assigns the NSGAIII selection operator to BaseDecompositionEA.
 Parameters:
problem (MOProblem) – The problem class object specifying the details of the problem.
population_size (int, optional) – The desired population size, by default None, which sets up a default value of population size depending upon the dimensionaly of the problem.
population_params (Dict, optional) – The parameters for the population class, by default None. See desdeo_emo.population.Population for more details.
initial_population (Population, optional) – An initial population class, by default None. Use this if you want to set up a specific starting population, such as when the output of one EA is to be used as the input of another.
lattice_resolution (int, optional) – The number of divisions along individual axes in the objective space to be used while creating the reference vector lattice by the simplex lattice design. By default None
selection_type (str, optional) – One of [“mean”, “optimistic”, “robust”]. To be used in datadriven optimization. To be used only with surrogate models which return an “uncertainity” factor. Using “mean” is equivalent to using the mean predicted values from the surrogate models and is the default case. Using “optimistic” results in using (mean  uncertainity) values from the the surrogate models as the predicted value (in case of minimization). It is (mean + uncertainity for maximization). Using “robust” is the opposite of using “optimistic”.
a_priori (bool, optional) – A bool variable defining whether a priori preference is to be used or not. By default False
interact (bool, optional) – A bool variable defining whether interactive preference is to be used or not. By default False
n_iterations (int, optional) – The total number of iterations to be run, by default 10. This is not a hard limit and is only used for an internal counter.
n_gen_per_iter (int, optional) – The total number of generations in an iteration to be run, by default 100. This is not a hard limit and is only used for an internal counter.
total_function_evaluations (int, optional) – Set an upper limit to the total number of function evaluations. When set to zero, this argument is ignored and other termination criteria are used.
 class desdeo_emo.EAs.PPGA(problem, population_size: int = 100, population_params=None, initial_population=None, n_iterations: int = 10, n_gen_per_iter: int = 10, predator_pop_size: int = 50, prey_max_moves: int = 10, prob_prey_move: float = 0.3, offspring_place_attempts: int = 10, kill_interval: int = 7, max_rank: int = 20, neighbourhood_radius: int = 3)
Bases:
desdeo_emo.EAs.BaseEA.BaseEA
PredatoryPrey genetic algorithm.
A population of prey signify the various models or solutions to the problem at hand. Weaker prey, i.e. bad models or solutions, are killed by predators. The predators and prey are placed in a lattice, in which they are free to roam.
In each generation, each predator gets a certain number of turns to move about and hunt in its neighbourhood, killing the weaker prey, according to a fitness criteria. After this, each prey gets a certain number of moves to pursue a random walk and to reproduce with other prey. Each reproduction step generates two new prey from two parents, by crossing over their attributes and adding random mutations. After each prey has completed its move, the whole process starts again.
As the weaker individuals get eliminated in each generation, the population as a whole becomes more fit, i.e. the individuals get closer to the true paretooptimal solutions.
If you have any questions about the code, please contact:
Bhupinder Saini: bhupinder.s.saini@jyu.fi Project researcher at University of Jyväskylä.
 Parameters:
population (object) – The population object
Notes
The algorithm has been created earlier in MATLAB, and this Python implementation has been using that code as a basis. See references [4] for the study during which the original MATLAB version was created. Python code has been written by Niko Rissanen under the supervision of professor Nirupam Chakraborti.
For the MATLAB implementation, see: N. Chakraborti. DataDriven BiObjective Genetic Algorithms EvoNN and BioGP and Their Applications in Metallurgical and Materials Domain. In Datta, Shubhabrata, Davim, J. Paulo (eds.), Computational Approaches to Materials Design: Theoretical and Practical Aspects, pp. 346369, 2016.
References
[1] Laumanns, M., Rudolph, G., & Schwefel, H. P. (1998). A spatial predatorprey approach to multiobjective optimization: A preliminary study. In International Conference on Parallel Problem Solving from Nature (pp. 241249). Springer, Berlin, Heidelberg.
[2] Li, X. (2003). A realcoded predatorprey genetic algorithm for multiobjective optimization. In International Conference on Evolutionary MultiCriterion Optimization (pp. 207221). Springer, Berlin, Heidelberg.
[3] Chakraborti, N. (2014). Strategies for evolutionary data driven modeling in chemical and metallurgical Systems. In Applications of Metaheuristics in Process Engineering (pp. 89122). Springer, Cham.
[4] Pettersson, F., Chakraborti, N., & Saxén, H. (2007). A genetic algorithms based multiobjective neural net applied to noisy blast furnace data. Applied Soft Computing, 7(1), 387397.
 _next_gen()
Run one generation of PPGA.
Intended to be used by next_iteration.
 Parameters:
population ("Population") – Population object
 select(population, max_rank=20) list
Of the population, individuals lower than max_rank are selected. Return indices of selected individuals.
 Parameters:
population (Population) – Contains the current population and problem information.
max_rank (int) – Select only individuals lower than max_rank
 Returns:
List of indices of individuals to be selected.
 Return type:
list
 class desdeo_emo.EAs.TournamentEA(problem, initial_population: desdeo_emo.population.Population.Population, n_gen_per_iter: int = 10, n_iterations: int = 10, tournament_size: int = 5, population_size: int = 500)
Bases:
desdeo_emo.EAs.BaseEA.BaseEA
This class provides the basic structure for Evolutionary algorithms.
 start()
Mimics the structure of the mcdm methods. Returns the request objects from self.retuests().
 end()
Conducts nondominated sorting at the end of the evolution process :returns:
 The first element is a 2D array of the decision vectors of the nondominated solutions.
The second element is a 2D array of the corresponding objective values.
 Return type:
tuple
 _next_gen()
Run one generation of decomposition based EA.
This method leaves method.params unchanged. Intended to be used by next_iteration.
 Parameters:
population ("Population") – Population object
 select() list
 class desdeo_emo.EAs.IOPIS_NSGAIII(problem: desdeo_problem.MOProblem, population_size: int = None, population_params: Dict = None, initial_population: desdeo_emo.population.Population.Population = None, lattice_resolution: int = None, n_iterations: int = 10, n_gen_per_iter: int = 100, total_function_evaluations: int = 0, use_surrogates: bool = False)[source]
Bases:
BaseIOPISDecompositionEA
The Base class for decomposition based EAs.
This class contains most of the code to set up the parameters and operators. It also contains the logic of a simple decomposition EA.
 Parameters:
problem (MOProblem) – The problem class object specifying the details of the problem.
selection_operator (Type[SelectionBase], optional) – The selection operator to be used by the EA, by default None.
population_size (int, optional) – The desired population size, by default None, which sets up a default value of population size depending upon the dimensionaly of the problem.
population_params (Dict, optional) – The parameters for the population class, by default None. See desdeo_emo.population.Population for more details.
initial_population (Population, optional) – An initial population class, by default None. Use this if you want to set up a specific starting population, such as when the output of one EA is to be used as the input of another.
lattice_resolution (int, optional) – The number of divisions along individual axes in the objective space to be used while creating the reference vector lattice by the simplex lattice design. By default None
a_priori (bool, optional) – A bool variable defining whether a priori preference is to be used or not. By default False
interact (bool, optional) – A bool variable defining whether interactive preference is to be used or not. By default False
n_iterations (int, optional) – The total number of iterations to be run, by default 10. This is not a hard limit and is only used for an internal counter.
n_gen_per_iter (int, optional) – The total number of generations in an iteration to be run, by default 100. This is not a hard limit and is only used for an internal counter.
total_function_evaluations (int, optional) – Set an upper limit to the total number of function evaluations. When set to zero, this argument is ignored and other termination criteria are used.
 class desdeo_emo.EAs.IOPIS_RVEA(problem: desdeo_problem.MOProblem, population_size: int = None, population_params: Dict = None, initial_population: desdeo_emo.population.Population.Population = None, alpha: float = None, lattice_resolution: int = None, n_iterations: int = 10, n_gen_per_iter: int = 100, total_function_evaluations: int = 0, time_penalty_component: str  float = None, use_surrogates: bool = False)[source]
Bases:
BaseIOPISDecompositionEA
,desdeo_emo.EAs.RVEA.RVEA
The python version reference vector guided evolutionary algorithm.
Most of the relevant code is contained in the super class. This class just assigns the APD selection operator to BaseDecompositionEA.
NOTE: The APD function had to be slightly modified to accomodate for the fact that this version of the algorithm is interactive, and does not have a set termination criteria. There is a time component in the APD penalty function formula of the type: (t/t_max)^alpha. As there is no set t_max, the formula has been changed. See below, the documentation for the argument: penalty_time_component
See the details of RVEA in the following paper
R. Cheng, Y. Jin, M. Olhofer and B. Sendhoff, A Reference Vector Guided Evolutionary Algorithm for Manyobjective Optimization, IEEE Transactions on Evolutionary Computation, 2016
 Parameters:
problem (MOProblem) – The problem class object specifying the details of the problem.
population_size (int, optional) – The desired population size, by default None, which sets up a default value of population size depending upon the dimensionaly of the problem.
population_params (Dict, optional) – The parameters for the population class, by default None. See desdeo_emo.population.Population for more details.
initial_population (Population, optional) – An initial population class, by default None. Use this if you want to set up a specific starting population, such as when the output of one EA is to be used as the input of another.
alpha (float, optional) – The alpha parameter in the APD selection mechanism. Read paper for details.
lattice_resolution (int, optional) – The number of divisions along individual axes in the objective space to be used while creating the reference vector lattice by the simplex lattice design. By default None
a_priori (bool, optional) – A bool variable defining whether a priori preference is to be used or not. By default False
interact (bool, optional) – A bool variable defining whether interactive preference is to be used or not. By default False
n_iterations (int, optional) – The total number of iterations to be run, by default 10. This is not a hard limit and is only used for an internal counter.
n_gen_per_iter (int, optional) – The total number of generations in an iteration to be run, by default 100. This is not a hard limit and is only used for an internal counter.
total_function_evaluations (int, optional) – Set an upper limit to the total number of function evaluations. When set to zero, this argument is ignored and other termination criteria are used.
penalty_time_component (Union[str, float], optional) – The APD formula had to be slightly changed. If penalty_time_component is a float between [0, 1], (t/t_max) is replaced by that constant for the entire algorithm. If penalty_time_component is “original”, the original intent of the paper is followed and (t/t_max) is calculated as (current generation count/total number of generations). If penalty_time_component is “function_count”, (t/t_max) is calculated as (current function evaluation count/total number of function evaluations) If penalty_time_component is “interactive”, (t/t_max) is calculated as (Current gen count within an iteration/Total gen count within an iteration). Hence, time penalty is always zero at the beginning of each iteration, and one at the end of each iteration. Note: If the penalty_time_component ever exceeds one, the value one is used as the penalty_time_component. If no value is provided, an appropriate default is selected. If interact is true, penalty_time_component is “interactive” by default. If interact is false, but total_function_evaluations is provided, penalty_time_component is “function_count” by default. If interact is false, but total_function_evaluations is not provided, penalty_time_component is “original” by default.
 _time_penalty_constant()
Returns the constant time penalty value.
 _time_penalty_original()
Calculates the appropriate time penalty value, by the original formula.
 _time_penalty_interactive()
Calculates the appropriate time penalty value.
 _time_penalty_function_count()
Calculates the appropriate time penalty value.
 class desdeo_emo.EAs.MOEA_D(problem: desdeo_problem.MOProblem, n_neighbors: int = 20, n_parents: int = 2, use_repair: bool = True, initial_population: desdeo_emo.population.Population.Population = None, population_size: int = None, population_params: Dict = None, lattice_resolution: int = None, interact: bool = False, n_iterations: int = 10, n_gen_per_iter: int = 100, use_surrogates: bool = False, total_function_evaluations: int = 0, scalarization_function: desdeo_tools.scalarization.MOEADSF = PBI(), keep_archive: bool = False, save_non_dominated: bool = False)[source]
Bases:
desdeo_emo.EAs.BaseEA.BaseDecompositionEA
Python implementation of MOEA/D
in IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712731, Dec. 2007, doi: 10.1109/TEVC.2007.892759.
 Parameters:
problem (MOProblem) – The problem class object specifying the details of the problem.
scalarization_function (MOEADSF) – The scalarization function to compare the solutions. Some implementations can be found in desdeotools/scalarization/MOEADSF. By default it uses the PBI function.
n_neighbors (int, optional) – Number of reference vectors considered in the neighborhoods creation. The default number is 20.
population_params (Dict, optional) – The parameters for the population class, by default None. See desdeo_emo.population.Population for more details.
initial_population (Population, optional) – An initial population class, by default None. Use this if you want to set up a specific starting population, such as when the output of one EA is to be used as the input of another.
lattice_resolution (int, optional) – The number of divisions along individual axes in the objective space to be used while creating the reference vector lattice by the simplex lattice design. By default None
n_parents (int, optional) – Number of individuals considered for the generation of offspring solutions. The default option is 2.
a_priori (bool, optional) – A bool variable defining whether a priori preference is to be used or not. By default False
interact (bool, optional) – A bool variable defining whether interactive preference is to be used or not. By default False
use_surrogates (bool, optional) – A bool variable defining whether surrogate problems are to be used or not. By default False
n_iterations (int, optional) – The total number of iterations to be run, by default 10. This is not a hard limit and is only used for an internal counter.
n_gen_per_iter (int, optional) – The total number of generations in an iteration to be run, by default 100. This is not a hard limit and is only used for an internal counter.
total_function_evaluations (int, optional) – Set an upper limit to the total number of function evaluations. When set to zero, this argument is ignored and other termination criteria are used.
 _next_gen()
Run one generation of decomposition based EA. Intended to be used by next_iteration.
 _select(current_neighborhood: int) list
Describe a selection mechanism. Return indices of selected individuals.
 Returns:
List of indices of individuals to be selected.
 Return type:
list
 class desdeo_emo.EAs.IBEA(problem: desdeo_problem.MOProblem, population_size: int, initial_population: desdeo_emo.population.Population.Population = None, a_priori: bool = False, interact: bool = False, population_params: Dict = None, n_iterations: int = 10, n_gen_per_iter: int = 100, total_function_evaluations: int = 0, use_surrogates: bool = False, kappa: float = 0.05, indicator: Callable = epsilon_indicator)[source]
Bases:
desdeo_emo.EAs.BaseIndicatorEA.BaseIndicatorEA
Python Implementation of IBEA.
Most of the relevant code is contained in the super class. This class just assigns the EnviromentalSelection operator to BaseIndicatorEA.
 Parameters:
problem (MOProblem) – The problem class object specifying the details of the problem.
population_size (int, optional) – The desired population size, by default None, which sets up a default value of population size depending upon the dimensionaly of the problem.
population_params (Dict, optional) – The parameters for the population class, by default None. See desdeo_emo.population.Population for more details.
initial_population (Population, optional) – An initial population class, by default None. Use this if you want to set up a specific starting population, such as when the output of one EA is to be used as the input of another.
a_priori (bool, optional) – A bool variable defining whether a priori preference is to be used or not. By default False
interact (bool, optional) – A bool variable defining whether interactive preference is to be used or not. By default False
n_iterations (int, optional) – The total number of iterations to be run, by default 10. This is not a hard limit and is only used for an internal counter.
n_gen_per_iter (int, optional) – The total number of generations in an iteration to be run, by default 100. This is not a hard limit and is only used for an internal counter.
total_function_evaluations (int, optional) – Set an upper limit to the total number of function evaluations. When set to zero, this argument is ignored and other termination criteria are used.
use_surrogates (bool, optional) – A bool variable defining whether surrogate problems are to be used or not. By default False
kappa (float, optional) – Fitness scaling value for indicators. By default 0.05.
indicator (Callable, optional) – Quality indicator to use in indicatorEAs. By default in IBEA this is additive epsilon indicator.
 _fitness_assignment(fitnesses)
Performs the fitness assignment of the individuals.
 _environmental_selection(fitnesses, worst_index)
Selects the worst member of population, then updates the population members fitness values compared to the worst individual. Worst individual is removed from the population.