macop.operators.discrete.crossovers

Crossover implementations for discrete solutions kind

Classes

RandomSplitCrossover()	Crossover implementation which generated new solution by randomly splitting best solution and current solution
SimpleCrossover()	Crossover implementation which generated new solution by splitting at mean size best solution and current solution

class macop.operators.discrete.crossovers.RandomSplitCrossover

Crossover implementation which generated new solution by randomly splitting best solution and current solution

kind

{KindOperator} – specify the kind of operator

Example:

>>> # operators import
>>> from macop.operators.discrete.crossovers import RandomSplitCrossover
>>> from macop.operators.discrete.mutators import SimpleMutation
>>> # policy import
>>> from macop.policies.reinforcement import UCBPolicy
>>> # solution and algorithm
>>> from macop.solutions.discrete import BinarySolution
>>> from macop.algorithms.mono import IteratedLocalSearch
>>> # evaluator import
>>> from macop.evaluators.discrete.mono import KnapsackEvaluator
>>> # evaluator initialization (worths objects passed into data)
>>> worths = [ random.randint(0, 20) for i in range(10) ]
>>> evaluator = KnapsackEvaluator(data={'worths': worths})
>>> # validator specification (based on weights of each objects)
>>> weights = [ random.randint(20, 30) for i in range(10) ]
>>> validator = lambda solution: True if sum([weights[i] for i, value in enumerate(solution._data) if value == 1]) < 200 else False
>>> # initializer function with lambda function
>>> initializer = lambda x=10: BinarySolution.random(x, validator)
>>> # operators list with crossover and mutation
>>> random_split_crossover = RandomSplitCrossover()
>>> simple_mutation = SimpleMutation()
>>> operators = [random_split_crossover, simple_mutation]
>>> policy = UCBPolicy(operators)
>>> algo = IteratedLocalSearch(initializer, evaluator, operators, policy, validator, maximise=True, verbose=False)
>>> # using best solution, simple crossover is applied
>>> best_solution = algo.run(100)
>>> list(best_solution._data)
[1, 1, 1, 0, 1, 0, 1, 1, 1, 0]
>>> new_solution = initializer()
>>> mutate_solution = random_split_crossover.apply(new_solution)
>>> list(mutate_solution._data)
[1, 0, 0, 1, 1, 0, 0, 1, 0, 0]

apply(solution)

Create new solution based on best solution found and solution passed as parameter

Parameters

solution – {Solution} – the solution to use for generating new solution

Returns

{Solution} – new generated solution

class macop.operators.discrete.crossovers.SimpleCrossover

Crossover implementation which generated new solution by splitting at mean size best solution and current solution

kind

{Algorithm} – specify the kind of operator

Example:

>>> # operators import
>>> from macop.operators.discrete.crossovers import SimpleCrossover
>>> from macop.operators.discrete.mutators import SimpleMutation
>>> # policy import
>>> from macop.policies.reinforcement import UCBPolicy
>>> # solution and algorithm
>>> from macop.solutions.discrete import BinarySolution
>>> from macop.algorithms.mono import IteratedLocalSearch
>>> # evaluator import
>>> from macop.evaluators.discrete.mono import KnapsackEvaluator
>>> # evaluator initialization (worths objects passed into data)
>>> worths = [ random.randint(0, 20) for i in range(10) ]
>>> evaluator = KnapsackEvaluator(data={'worths': worths})
>>> # validator specification (based on weights of each objects)
>>> weights = [ random.randint(20, 30) for i in range(10) ]
>>> validator = lambda solution: True if sum([weights[i] for i, value in enumerate(solution._data) if value == 1]) < 200 else False
>>> # initializer function with lambda function
>>> initializer = lambda x=10: BinarySolution.random(x, validator)
>>> # operators list with crossover and mutation
>>> simple_crossover = SimpleCrossover()
>>> simple_mutation = SimpleMutation()
>>> operators = [simple_crossover, simple_mutation]
>>> policy = UCBPolicy(operators)
>>> algo = IteratedLocalSearch(initializer, evaluator, operators, policy, validator, maximise=True, verbose=False)
>>> # using best solution, simple crossover is applied
>>> best_solution = algo.run(100)
>>> list(best_solution._data)
[1, 1, 0, 1, 0, 1, 1, 1, 0, 1]
>>> new_solution = initializer()
>>> mutate_solution = simple_crossover.apply(new_solution)
>>> list(mutate_solution._data)
[0, 1, 0, 0, 0, 1, 1, 1, 0, 1]

apply(solution)

Create new solution based on best solution found and solution passed as parameter

Parameters

solution – {Solution} – the solution to use for generating new solution

Returns

{Solution} – new generated solution