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feat: Added seed parameter to all funcs
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- Added seed parameter for all funcs with default randint()
- All funcs can now count and return Number of Function Evaluations (NFE)
- All funcs return additional seed and nfe values
- Count r_nfe as local search nfe and include it in actual nfe
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@Agrover112
Agrover112 committed Jun 20, 2021
1 parent f211e7a commit 5e77054
Showing 1 changed file with 36 additions and 16 deletions.
52 changes: 36 additions & 16 deletions algorithms.py
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@@ -1,14 +1,13 @@
import heapq
import math
import random
random.seed(10)
import sys

"""Add option to select mutation type in GA method.
Add bit flip mutation
Wrapp all different mutation types in a class XD
"""
def random_search(domain, fitness_function, init=[], epochs=100):
def random_search(domain, fitness_function,seed=random.randint(10,100), init=[], epochs=100):
""" Random search algorithm implemented
Args:
Expand All @@ -22,9 +21,10 @@ def random_search(domain, fitness_function, init=[], epochs=100):
int: The final cost after running the algorithm,
list: List containing all costs during all epochs.
"""

random.seed(seed)
best_cost = sys.maxsize
scores = []
nfe=0
if len(init) > 0:
solution = init
else:
Expand All @@ -35,14 +35,15 @@ def random_search(domain, fitness_function, init=[], epochs=100):
solution = [random.randint(domain[i][0], domain[i][1])
for i in range(len(domain))]
cost = fitness_function(solution, 'FCO')
nfe+=1
if cost < best_cost:
best_cost = cost
best_solution = solution
scores.append(best_cost)
return best_solution, best_cost, scores
return best_solution, best_cost,scores,nfe,seed


def hill_climb(domain, fitness_function, init=[], epochs=100):
def hill_climb(domain, fitness_function,seed=random.randint(10,100), init=[], epochs=100):
""" Simple Hill Climbing algorithm implemented
Args:
Expand All @@ -56,8 +57,10 @@ def hill_climb(domain, fitness_function, init=[], epochs=100):
int: The final cost after running the algorithm,
list: List containing all costs during all epochs.
"""
random.seed(seed)
count = 0
scores = []
nfe=0
if len(init) > 0:
solution = init
else:
Expand All @@ -76,23 +79,25 @@ def hill_climb(domain, fitness_function, init=[], epochs=100):
solution[0:i] + [solution[i] - 1] + solution[i + 1:])

actual = fitness_function(solution, 'FCO')
nfe+=1
best = actual
for i in range(len(neighbors)):
count += 1
cost = fitness_function(neighbors[i], 'FCO')
nfe+=1
if cost < best:
best = cost
solution = neighbors[i]
scores.append(best)

if best == actual:
print('Count: ', count)
#print('NFE: ',nfe)
break

return solution, best, scores
return solution, best, scores,nfe,seed


def simulated_annealing(domain, fitness_function, init=[], temperature=50000.0, cooling=0.95, step=1):
def simulated_annealing(domain, fitness_function,seed=random.randint(10,100), init=[], temperature=50000.0, cooling=0.95, step=1):
""" Simulated annealing algorithm implemented with temeperature and cooling parameters.
Expand All @@ -110,7 +115,9 @@ def simulated_annealing(domain, fitness_function, init=[], temperature=50000.0,
int: The final cost after running the algorithm,
list: List containing all costs during all epochs.
"""
random.seed(seed)
count = 0
nfe=0
scores = []
simulated_annealing.temp = []

Expand All @@ -132,7 +139,9 @@ def simulated_annealing(domain, fitness_function, init=[], temperature=50000.0,

count += 1
cost = fitness_function(solution, 'FCO')
nfe+=1
cost_temp = fitness_function(temp_solution, 'FCO')
nfe+=1
prob = pow(math.e, (-cost_temp - cost) / temperature)
best = cost
if (cost_temp < cost or random.random() < prob):
Expand All @@ -144,7 +153,7 @@ def simulated_annealing(domain, fitness_function, init=[], temperature=50000.0,
simulated_annealing.temp.append(temperature)

print('Count: ', count)
return solution, best, scores
return solution, best, scores,nfe,seed


def mutation(domain, step, solution):
Expand Down Expand Up @@ -189,9 +198,9 @@ def crossover(domain, solution_1, solution_2):
return solution_1[0:gene] + solution_2[gene:]


def genetic_algorithm(domain, fitness_function, init=[], population_size=100, step=1,
def genetic_algorithm(domain, fitness_function,seed=random.randint(10,100), init=[], population_size=100, step=1,
probability_mutation=0.2, elitism=0.2,
number_generations=500, search=False):
number_generations=500, search=True):
""" Genetic algorithm implemented with elitisim.
Expand All @@ -211,11 +220,15 @@ def genetic_algorithm(domain, fitness_function, init=[], population_size=100, st
int: The final cost after running the algorithm,
list: List containing all costs during all epochs.
"""
random.seed(seed)
population = []
scores = []
nfe=0
for i in range(population_size):
if search == True:
solution = random_search(domain, fitness_function)
solution, b_c,sc,r_nfe,s = random_search(domain, fitness_function,seed)
print(r_nfe)
nfe+=r_nfe
if len(init) > 0:
solution = init
else:
Expand All @@ -229,11 +242,13 @@ def genetic_algorithm(domain, fitness_function, init=[], population_size=100, st
for i in range(number_generations):
costs = [(fitness_function(individual, 'FCO'), individual)
for individual in population]
nfe+=1
# costs.sort()
heapq.heapify(costs)
ordered_individuals = [individual for (cost, individual) in costs]
population = ordered_individuals[0:number_elitism]
scores.append(fitness_function(population[0], 'FCO'))
nfe+=1
while len(population) < population_size:
if random.random() < probability_mutation:
m = random.randint(0, number_elitism)
Expand All @@ -244,10 +259,10 @@ def genetic_algorithm(domain, fitness_function, init=[], population_size=100, st
i2 = random.randint(0, number_elitism)
population.append(
crossover(domain, ordered_individuals[i1], ordered_individuals[i2]))
return costs[0][1], costs[0][0], scores
return costs[0][1], costs[0][0], scores,nfe,seed


def genetic_algorithm_reversed(domain, fitness_function, init=[], population_size=100, step=1,
def genetic_algorithm_reversed(domain, fitness_function,seed=random.randint(10,100), init=[], population_size=100, step=1,
probability_mutation=0.2, elitism=0.2,
number_generations=500, search=False):
""" Genetic algorithm implemented with elitisim.
Expand All @@ -269,11 +284,14 @@ def genetic_algorithm_reversed(domain, fitness_function, init=[], population_siz
int: The final cost after running the algorithm,
list: List containing all costs during all epochs.
"""
random.seed(seed)
population = []
scores = []
nfe=0
for i in range(population_size):
if search == True:
solution = random_search(domain, fitness_function)
solution, b_c,sc,r_nfe,s = random_search(domain, fitness_function,seed)
nfe+=r_nfe
if len(init) > 0:
solution = init
else:
Expand All @@ -287,11 +305,13 @@ def genetic_algorithm_reversed(domain, fitness_function, init=[], population_siz
for i in range(number_generations):
costs = [(fitness_function(individual, 'FCO'), individual)
for individual in population]
nfe+=1
# costs.sort()
heapq.heapify(costs)
ordered_individuals = [individual for (cost, individual) in costs]
population = ordered_individuals[0:number_elitism]
scores.append(fitness_function(population[0], 'FCO'))
nfe+=1
while len(population) < population_size:
if random.random() < probability_mutation:
i1 = random.randint(0, number_elitism)
Expand All @@ -303,6 +323,6 @@ def genetic_algorithm_reversed(domain, fitness_function, init=[], population_siz
population.append(
mutation(domain, step, ordered_individuals[m]))

return costs[0][1], costs[0][0], scores
return costs[0][1], costs[0][0], scores,nfe,seed


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