def __init__(self, **kwargs):
# !
# Here all algorithm parameters needed no matter what is problem is passed are defined
# problem dependent initialization happens in setup(problem, **kwargs)
# !
super().__init__()
# prints the compile warning if enabled
FunctionLoader.get_instance()
# !
# DEFAULT SETTINGS OF ALGORITHM
# !
# the termination criterion to be used by the algorithm - might be specific for an algorithm
self.termination = kwargs.get("termination")
# set the display variable supplied to the algorithm - might be specific for an algorithm
self.display = kwargs.get("display")
# callback to be executed each generation
self.callback = kwargs.get("callback")
# !
# Attributes to be set later on for each problem run
# !
# the optimization problem as an instance
self.problem = None
# whether the algorithm should finally return the least infeasible solution if no feasible found
self.return_least_infeasible = None
# whether the history should be saved or not
self.save_history = None
# whether the algorithm should print output in this run or not
self.verbose = None
# the random seed that was used
self.seed = None
# an algorithm can defined the default termination which can be overwritten
self.default_termination = None
# whether the algorithm as terminated or not
self.has_terminated = None
# the pareto-front of the problem - if it exist or passed
self.pf = None
# the function evaluator object (can be used to inject code)
self.evaluator = None
# the current number of generation or iteration
self.n_gen = None
# the history object which contains the list
self.history = None
# the current solutions stored - here considered as population
self.pop = None
# a placeholder object for implementation to store solutions in each iteration
self.off = None
# the optimum found by the algorithm
self.opt = None
# can be used to store additional data in submodules
self.data = {}
# if the initialized method has been called before or not
self.is_initialized = False
# the time when the algorithm has been setup for the first time
self.start_time = None
def __init__(self,
callback=None,
display=None,
termination=None,
return_least_infeasible=False,
**kwargs):
# !
# Here all algorithm parameters needed no matter what is problem is passed are defined
# problem dependent initialization happens in initialize(problem, **kwargs)
# !
super().__init__()
# prints the compile warning if enabled
FunctionLoader.get_instance()
# function used to display attributes
# other attributes of the algorithm
self.callback = callback
self.display = None
self.return_least_infeasible = return_least_infeasible
# !
# Attributes to be set later on for each problem run
# !
# the optimization problem as an instance
self.problem = None
# the termination criterion of the algorithm
self.termination = termination
# an algorithm can defined the default termination which can be overwritten
self.default_termination = None
# the random seed that was used
self.seed = None
# the pareto-front of the problem - if it exist or passed
self.pf = None
# the function evaluator object (can be used to inject code)
self.evaluator = None
# the current number of generation or iteration
self.n_gen = None
# whether the history should be saved or not
self.save_history = None
# the history object which contains the list
self.history = None
# the current solutions stored - here considered as population
self.pop = None
# the optimum found by the algorithm
self.opt = None
# whether the algorithm should print output in this run or not
self.verbose = None
# set the display variable supplied to the algorithm
self.display = display
# can be used to store additional data in submodules
self.data = {}