def __init__(self, w, target_OFE_budgets, gammaBudget, updateNeighbours,
extra_termination_critea = [],):
'''
Required Args
* w - weights vector used for scalarization.
* initial_target_OFE_budgets - OFE budgets at which tuning is desired to take place for the subproblem
* gammaBudget - number of application layer evaluations to use on subproblem. i.e. if the algorithm being tuned is run 3 times with an OFE budget of 180 on 2 subproblem (w has 2 non-zero elements), then the gamma usage is 1080.
* updateNeighbours - subproblems who PFAs are in the update neighbourhood when CPV tuples for this subproblem are evaluated
'''
assert type(w) == numpy.ndarray
assert type(target_OFE_budgets) == numpy.ndarray
assert type(gammaBudget) == int
assert type(updateNeighbours) == list
self.initialized = False
self.w = w
self.target_OFE_budgets = copy.deepcopy(target_OFE_budgets)
self.min_target_OFE_budget = min(target_OFE_budgets)
self.max_target_OFE_budget = max(target_OFE_budgets)
self.gamma = 0
self.gammaBudget = gammaBudget
self.updateNeighbours = updateNeighbours
self.extra_termination_critea = extra_termination_critea
self.terminated_due_extra_termination_critea = False
self.PFA = paretoArchive2D_polynimal_simularity_checking() #Pareto-optimal Front Approximation
self.PFA_history = PFA_history_recorder()
self.objective_transform_m = numpy.ones(len(w))
self.objective_transform_c = numpy.zeros(len(w)) # z' = m*z + c
