Documentation¶

macop¶

`macop.algorithms.Algorithm`	Abstract Algorithm class used as basic algorithm implementation with some specific initialization
`macop.algorithms.mono.IteratedLocalSearch`	Iterated Local Search Algorithm implementation
`macop.algorithms.mono.LocalSearch`	Local Search algorithm

`macop.callbacks.BasicCheckpoint`	Basic Checkpoint class implementation
`macop.callbacks.MultiCheckpoint`	Multi Checkpoint class implementation
`macop.callbacks.ParetoCheckpoint`	Pareto front Checkpoint class implementation
`macop.callbacks.Callback`	Abstract Checkpoint class

Python evaluator function example

`macop.operators.crossovers.Crossover`	Abstract Crossover class
`macop.operators.crossovers.RandomSplitCrossover`	Crossover implementation which generated new solution by randomly splitting best solution and current solution
`macop.operators.crossovers.SimpleCrossover`	Crossover implementation which generated new solution by splitting at mean size best solution and current solution
`macop.operators.mutators.Mutation`	Abstract Mutation class
`macop.operators.mutators.SimpleBinaryMutation`	Mutation implementation for binary solution, swap bit randomly from solution
`macop.operators.mutators.SimpleMutation`	Mutation implementation for binary solution, swap two bits randomly from solution
`macop.operators.policies.Policy`	Abstract class which is used for applying strategy when selecting and applying operator
`macop.operators.policies.RandomPolicy`	Policy class implementation which is used for selecting operator randomly
`macop.operators.policies.UCBPolicy`	Policy class implementation which is used for selecting operator using Upper Confidence Bound
`macop.operators.Operator`	Abstract Operator class

`macop.solutions.BinarySolution`	Binary solution class implementation
`macop.solutions.CombinatoryIntegerSolution`	Combinatory integer solution class implementation
`macop.solutions.IntegerSolution`	Integer solution class implementation
`macop.solutions.Solution`	Abstract solution class