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Genetic Algorithm: Threshold #680

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Jan 8, 2018
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Genetic Algorithm: Threshold #680

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24 changes: 18 additions & 6 deletions search.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,14 +23,14 @@

class Problem(object):

"""The abstract class for a formal problem. You should subclass
"""The abstract class for a formal problem. You should subclass
this and implement the methods actions and result, and possibly
__init__, goal_test, and path_cost. Then you will create instances
of your subclass and solve them with the various search functions."""

def __init__(self, initial, goal=None):
"""The constructor specifies the initial state, and possibly a goal
state, if there is a unique goal. Your subclass's constructor can add
state, if there is a unique goal. Your subclass's constructor can add
other arguments."""
self.initial = initial
self.goal = goal
Expand Down Expand Up @@ -708,14 +708,26 @@ def genetic_algorithm(population, fitness_fn, gene_pool=[0, 1], f_thres=None, ng
population = [mutate(recombine(*select(2, population, fitness_fn)), gene_pool, pmut)
for i in range(len(population))]

if f_thres:
fittest_individual = argmax(population, key=fitness_fn)
if fitness_fn(fittest_individual) >= f_thres:
return fittest_individual
fittest_individual = fitness_threshold(fitness_fn, f_thres, population)
if fittest_individual:
return fittest_individual


return argmax(population, key=fitness_fn)


def fitness_threshold(fitness_fn, f_thres, population):
if not f_thres:
return None

fittest_individual = argmax(population, key=fitness_fn)
if fitness_fn(fittest_individual) >= f_thres:
return fittest_individual

return None



def init_population(pop_number, gene_pool, state_length):
"""Initializes population for genetic algorithm
pop_number : Number of individuals in population
Expand Down