1. How to easily find the optimal solution
without exhaustive search
using Genetic Algorithms
Viacheslav Kakovskyi — Kyiv.py #16
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2. Me!
@kakovskyi
Python Developer at SoftServe
Recent projects:
● Backend for instant messenger
● Backend for embedded system for traffic analysis
● Software for personal nutrition planning
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3. Agenda
● Cases from my experience
● Terminology
● Genetic Algorithms by example
● Theoretical background
● Genetic Algorithms in Python
● Pros and cons
● Tasks from real life
● Summary
● Further reading
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4. Software for personal nutrition planning
● Find a set of suboptimal personal nutrition plans
● Minimize difference between required macronutrients and energetic value for
suggested plan
● Genetic Algorithms used for generation of daily meals
● The problem is similar to Knapsack problem
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5. Embedded system for traffic analysis (R&D)
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Data from camera (LPR), 2D Data from radar, 2D

6. Embedded system for traffic analysis (R&D)
● Find suboptimal transition matrix (4x4)
● Challenge: surface relief
● Maximize intersection of data from LPR and converted data from radar
● Tried to use Genetic Algorithms for parametric optimization
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7. Math party
● Function - a rule
● Domain - a set of possible input values of a function
● Codomain - a set of possible output values of a function
● Extremum - maximum or minimum of a function for a given domain
● Local extremum - an extremum of some neighbourhood of domain
● Global extremum - an extremum of all domain
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8. Genetics party
● Genotype - a set of genes of an instance
● Phenotype - a set of features of an instance
● Fitness-function - a function which describes adaptability of an instance
● Generation defines a lifetime of instances
● Population - a set of all instances which exist in the same area
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9. Genetics party
● Selection - a stage of GA when instances are chosen from a population for
later breeding
● Crossover - an operator used to vary chromosomes from one generation to
the next
● Mutation - an operator used to maintain genetic diversity from one generation
of a population to the next
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10. Genetic Algorithms by example
● Problem: find a solution of a linear Diophantine equation
● The equation: 1027x + 712y = 1
● D(x) = [-1 500; 1 500]
● D(y) = [-1 500; 1 500]
Analytical solution:
● http://mathworld.wolfram.com/DiophantineEquation.html
● http://math.stackexchange.com/a/20727
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11. Genetic Algorithms by example
● Problem type: minimization
● F(x,y) = |1027x+ 712y - 1|
● lambda x, y: abs(1027 * x + 712 * y - 1)
● IND_SIZE = 4
● NGEN = 100
● Initial population:
(-432, 325) -> 212265
(48, 1217) -> 915799
(-660, 1473) -> 370955
(22, 786) -> 582225
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12. Selection
● Ordering: 212265, 370955, 582225, 915799
● (48, 1217) -> 915799 is the worst adapted
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13. Crossover
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14. Crossover
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15. Mutation
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● Add a random integer from the range [-10, 10]
● MUTPB = 0.25

16. Mutation
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● Add a random integer from the range [-10, 10]
● MUTPB = 0.25

17. Selection 2
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18. Selection 2
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19. Last generation
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20. Flowchart
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21. Complexity
● IND_SIZE - size of population
● NGEN - number of generations
● CXPB - crossover probability
● MUTPB - mutation probability
Complexity ≈ IND_SIZE * NGEN * O(Fitness) * O(Selection) *
(CXPB * O(Crossover) + MUTPB * O(Mutation))
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22. Guidelines
● Define your fitness-function
● Choose the problem type
● Define the domain and NGEN
● Define how the initial population should be chosen and IND_SIZE
● Define the type of selection
● Define the type of crossover
● Define the type of mutation and MUTPB
● Try it!
● Define heuristics if needed
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23. Genetic Algorithms in Python
● DEAP
● Pyevolve
● PyBrain
● pygene
● pyeasyga
● pyneurgen
● pyislands
● inspyred
● tinyevolver
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24. DEAP: Distributed Evolutionary Algorithms in Python
● Batteries included
● Parallelization
● Genetic Programming
● Works on Python 2, Python 3 and PyPy
● Lots of scientific projects already use it
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25. DEAP by example
● Problem: find a solution of a linear Diophantine equation.
● The equation: 1027x+ 712y = 1
● D(x) = [-1 500; 1 500]
● D(y) = [-1 500; 1 500]
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26. DEAP by example
"""
The equation: 1027 * x + 712 * y = 1
D(x) = [-1 500; 1 500]
D(y) = [-1 500; 1 500]
"""
import random
import operator
from deap import tools, base, creator, algorithms
MIN, MAX = -1500, 1500
SOLUTION = [-165, 238]
VARIABLES = len(SOLUTION)
MUT_MIN, MUT_MAX = -10, 10
NGEN, IND_SIZE, CXPB, MUTPB, TRN_SIZE = 100, 50, 0.5, 0.25, 100
HALL_SIZE = 10
DEFAULT_MAIN_ARGS = NGEN, IND_SIZE, CXPB, MUTPB
BEST_INSTANCE_MSG = 'Best instance:'
NO_SOLUTION_MSG = 'No solution in integers. Distance is:'
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def fitness(instance):
x, y = instance
return abs(1027 * x + 712 * y - 1),
def spawn_instance():
return random.randint(MIN, MAX), random.randint(MIN, MAX)
def mutate(instance, mutpb):
if random.random() <= mutpb:
index = random.randint(0, len(instance) - 1)
instance[index] += random.randint(MUT_MIN, MUT_MAX)
return instance,
return instance,
def get_best_result(population):
if isinstance(population[0], list):
fitness_values = list(map(fitness, population))
index = fitness_values.index(min(fitness_values))
return population[index]
else:
return min(population, key=operator.attrgetter('fitness'))

27. DEAP by example
def terminate(population):
if fitness(get_best_result(population)) == (0, ):
raise StopIteration
return False
def distance_from_best_result(population):
result = get_best_result(population)
return fitness(result)[0]
def output(best_instance):
print(BEST_INSTANCE_MSG, best_instance)
distance = fitness(best_instance)
if distance:
print(NO_SOLUTION_MSG, distance)
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def setup(mutpb):
creator.create("FitnessMin", base.Fitness, weights=(-1,))
creator.create("Individual", list, fitness=creator.FitnessMin)
toolbox = base.Toolbox()
toolbox.register("attribute", random.randint, MIN, MAX)
toolbox.register("individual", tools.initRepeat, creator.Individual,
toolbox.attribute, n=VARIABLES)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("mate", tools.cxOnePoint)
toolbox.register("mutate", mutate, mutpb=mutpb)
toolbox.register("select", tools.selBest)
toolbox.register("evaluate", fitness)
return toolbox

28. DEAP by example
def main(ngen, ind_size, cxpb, mutpb):
toolbox = setup(ind_size)
population = toolbox.population(n=ind_size)
stats = tools.Statistics()
stats.register("best_instance_of_population", get_best_result)
stats.register("distance", distance_from_best_result)
stats.register("terminate", terminate)
halloffame = tools.HallOfFame(HALL_SIZE)
try:
algorithms.eaSimple(population, toolbox, cxpb, mutpb, ngen,
stats=stats, halloffame=halloffame)
except StopIteration:
pass
finally:
best_instance = halloffame[0]
output(best_instance)
return best_instance
if __name__ == '__main__':
main(*DEFAULT_MAIN_ARGS)
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github.com/viachk/ga-example-diophantine

29. Crossover types
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● cxOnePoint
● cxTwoPoint
● cxUniform
● cxPartialyMatched
● cxUniformPartialyMatched
● cxOrdered
● cxBlend
● cxESBlend
● cxESTwoPoint
● cxSimulatedBinary
● cxSimulatedBinaryBounded
● cxMessyOnePoint

30. Mutation types
● mutGaussian
● mutShuffleIndexes
● mutFlipBit
● mutPolynomialBounded
● mutUniformInt
● mutESLogNormal
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31. Selection types
● selTournament
● selRoulette
● selNSGA2
● selSPEA2
● selRandom
● selBest
● selWorst
● selTournamentDCD
● selDoubleTournament
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32. Pros of Genetic Algorithms
● easy in use
● lots lots of options for heuristics
● can solve majority of problems (somehow)
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33. Cons of Genetic Algorithms
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34. Local vs Global extrema
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35. Cons of Genetic Algorithms
● can't find the optimal solution
● terminates after the first local extremum
● computationally expensive
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36. Right tasks for Genetic Algorithms
● parametric optimization
● NP-complete problems
● machine learning
● bioinformatics
● when you need to design a prototype, looks like a proper solution for a
hackaton
https://en.wikipedia.org/wiki/List_of_genetic_algorithm_applications (78 items)
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37. Wrong tasks for Genetic Algorithms
● real-time processing
● an accurate method of solving the problem already exists
● huge amount of local extrema
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38. Summaries about Genetic Algorithms
● easy to start
● stochastic
● have lots of options for heuristics
● but the task should be formalized
● we have batteries for them in Python
● applicable in variety of real-life tasks
● terminate after the first local extremum
● computationally expensive
● for 99% of tasks find suboptimal solution
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39. Next steps
● Video: Genetic Algorithms by example (Eng)
● Video: Genetic Algorithms by example (Ru)
● Video: MIT AI class - Genetic Algorithms (Eng)
● Video: Kyiv AI 2012 - Evolutionary Algorithms (Ru)
● Video: Implementation of Genetic Algorithms (Ru)
● Video: A Framework for Genetic Algorithms Based on Hadoop (Eng)
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40. Next steps
● Pretty awesome book about Genetic Algorithms by Panchenko (Ru)
● Articles about Genetic Algorithms (Ru)
● Q&A about Genetic Algorithms (Ru)
● A Framework for Genetic Algorithms Based on Hadoop (Eng)
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41. Next steps
Try DEAP
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42. Thank you!
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Viacheslav Kakovskyi
viach.kakovskyi@gmail.com
@kakovskyi

43. Evolved antenna
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