flow

Some definitions loop for genetic algorithm.

Contains:

• Class: bgp.flow.BaseLoop

  one node mate and one tree mutate.

• Class: bgp.flow.MultiMutateLoop

  one node mate and (one tree mutate, one node Replacement mutate, shrink mutate, difference mutate).

• Class: bgp.flow.OnePointMutateLoop

  one node Replacement mutate: (keep height of tree)

• Class: bgp.flow.DimForceLoop

  Select with dimension : (keep dimension of tree)

if __name__ == "__main__":
    pset = SymbolSet()
    stop = lambda ind: ind.fitness.values[0] >= 0.880963
    bl = OnePointMutateLoop(pset=pset, gen=10, pop=1000, hall=1, batch_size=40, re_hall=3, \n
                    n_jobs=12, mate_prob=0.9, max_value=5, initial_min=1, initial_max=2, \n
                    mutate_prob=0.8, tq=True, dim_type="coef", stop_condition=stop,\n
                    re_Tree=0, store=False, random_state=1, verbose=True,\n
                    stats={"fitness_dim_max": ["max"], "dim_is_target": ["sum"]},\n
                    add_coef=True, inter_add=True, inner_add=False, cal_dim=True, vector_add=False,\n
                    personal_map=False)
    bl.run()

The Parameters, Methods, and Attributes for all loops are same.

• Parameters

  The Parameters is the same with skflow.SymbolLearning, except the ‘loop’ parameter in skflow.SymbolLearning.

• Methods

run:

run the loop.

The flow.BaseLoop.run is the base of skflow.SymbolicLearning.fit