The term Genetic Algorithms sounds intimidating to most, a subject obviously beyond the comprehension of anyone with fewer than two advanced degrees. But in truth, genetic algorithms are – like the biological evolution that inspired them – little more sophisticated than trial and error, and their power to solve problems with complex constraints makes them a tool worth having. This talk will bring genetic algorithms out of academic papers and expensive textbooks and teach those of us in industry what's needed to put them to use.

1. GENETIC ALGORITHMS
PROGRAMMING BY THE SEAT OF
YOUR GENES!
JEREMY FISHER
@THISISROOT
JUNE 29, 2016

2. Genetic algorithms are a biologically
inspired stochastic metaheuristic for
combinatorial search and optimization.

3. Genetic algorithms are a biologically
inspired stochastic metaheuristic for
combinatorial search and optimization
fancy method of trial-and-error.

4. EVOLUTIONARY ALGORITHM COMPLEXITY
Binary encoding
Single Point
Crossover
Uniform Crossover
Proportionate
Selection
Tournament
Selection
List Encoding
Value Encoding
Permutation
Encoding
Parallel GAs
Ordered
Crossover
Partially Matched
Crossover
Cycle Crossover
Tree Encoding
Edge
Recombination
Distributed GAs
Multi-Objective
Fitness Functions
Diploidism
Coevolutionary
Algorithms
Genetics-Based
Machine Learning
Genetic
Programming
Estimation of
Distribution
Algorithms
BEGINNER ADVANCEDINTERMEDIATE

5. GENETIC ALGORITHMS IN THE WILD
Operations Research
Sociology
Game Theory
Economics
Financial Trading
Biology

6. GENETIC ALGORITHM LIFECYCLE
1. Create a population of random
chromosomes (solutions).
2. Score each chromosome in the
population for fitness.
3. Create a new generation
through mutation and crossover.
4. Repeat until done.
5. Emit the fittest chromosome as
the solution.
⋆⋆⋆ ⋆⋆⋆⋆⋆⋆ ⋆⋆⋆
⋆⋆⋆ ⋆⋆⋆⋆⋆⋆ ⋆⋆⋆
⋆⋆⋆ ⋆⋆⋆⋆⋆⋆ ⋆⋆⋆
⋆⋆⋆

7. TERMINOLOGY
… 1 0 10Chromosome
a.k.a.
Genotype, Organism,
Creature, Member,
Individual, Solution
Allele
Locus

8. TERMINOLOGY
-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3
-0.8
0.8
1.6
2.4
3.2
Fitness Landscape

9. !
https://github.com/rawg/levis

10. 0/1 KNAPSACK PROBLEM
Given a set of items, each with a quantified
weight and value ($), what combination of
items will:
1. Fit inside a knapsack capable of carrying a
fixed amount of weight?
2. Maximize the value of the knapsack’s
payload?
15 lbs
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

11. ENCODING SCHEMES
1 0 0 1 0 1 1 0
BINARY ENCODING
10 3 22 19 65 97 22 41
VALUE ENCODING
A B C D E F G H
PERMUTATION ENCODING
G C F A B D E H
10 3 22 19
LIST ENCODING
19 65 97 35 41 10
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

12. 0/1 KNAPSACK PROBLEM
A
10 lbs | $11
B
4 lbs | $8
C
5 lbs | $6
D
2 lbs | $12
15 lbs
E
8 lbs | $5
F
2 lbs | $8
G
6 lbs | $4
H
7 lbs | $13
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

13. BINARY ENCODING
A B C D E F G H
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

14. BINARY ENCODING
A B C D E F G H
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

15. BINARY ENCODING
A B C D E F G H
10 lbs
$11
4 lbs
$8
5 lbs
$6
2 lbs
$12
8 lbs
$5
2 lbs
$8
6 lbs
$4
7 lbs
$13
1 0 1 0 1 0 1 0
WEIGHT: 29 lbs
VALUE: $26
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

16. INITIALIZE THE POPULATION
1 0 1 1 0 1 1 0
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

17. INITIALIZE THE POPULATION
1 0 1 1 0 1 1 01 1
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

18. INITIALIZE THE POPULATION
1 0 1 1 0 0 0 0
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

19. INITIALIZE THE POPULATION
0 0 1 1 0 1 0 1
7. SOLUTION6. MUTATION5. CROSSOVER4. FITNESS3. CREATION3. CREATION2. ENCODING1. PROBLEM 7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

20. FITNESS FUNCTION
Maximize value
Maximize weight to limit
def score(self, chromosome):
weight, value = self.assess(chromosome)
if weight > self.max_weight:
return 0.0
if value <= 0:
return 0.0
wt = 1 / (1 + self.max_weight - weight)
vl = 1 - 1 / value
return wt + vl * self.value_bias
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

21. FITNESS FUNCTION
def score(self, chromosome):
weight, value = self.assess(chromosome)
if weight > self.max_weight:
return 0.0
return 1 - 1 / value
Maximize value
Don’t exceed weight limit
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

22. FITNESS FUNCTION
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
-1
-0.75
-0.5
-0.25
0.25
0.5
0.75
1
1.25
1.5
1.75
2
2.25
2.5
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
y=1-1/x
y=x

23. PROPORTIONATE SELECTION
1 0 1 1 0 1 10 0 0 1 0 1 1 01 1 0 0 1 1 1 10 0 1 1 1 1 0 10
1 0 0 1 0 0 11 1 1 0 1 0 0 10 0 0 1 0 0 1 10 1 0 0 1 0 1 11
$40 $30 $25 $20
$20 $15 $10 $5
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

24. PROPORTIONATE SELECTION
$40 $30 $25 $20 $20 $15 $10 $5
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

25. PROPORTIONATE SELECTION
Most direct path to
convergence
Enables elitism
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

26. TOURNAMENT SELECTION
1 0 1 1 0 1 10 0 0 1 0 1 1 01 1 0 0 1 1 1 10 0 1 1 1 1 0 10
1 0 0 1 0 0 11 1 1 0 1 0 0 10 0 0 1 0 0 1 10 1 0 0 1 0 1 11
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

27. TOURNAMENT SELECTION
1 0 1 1 0 1 10 0 0 1 0 1 1 01 1 0 0 1 1 1 10 0 1 1 1 1 0 10
1 0 0 1 0 0 11 1 1 0 1 0 0 10 0 0 1 0 0 1 10 1 0 0 1 0 1 11
$30 $20
$15
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

28. TOURNAMENT SELECTION
1 0 1 1 0 1 10 0 0 1 0 1 1 01 1 0 0 1 1 1 10 0 1 1 1 1 0 10
1 0 0 1 0 0 11 1 1 0 1 0 0 10 0 0 1 0 0 1 10 1 0 0 1 0 1 11
$30 $20
$15
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

29. TOURNAMENT SELECTION
Fewer fitness function
invocations
Built-in scaling
1 0 1 1 0 1 10 0 0 1 0 1 1 01 1 0 0 1 1 1 10 0 1 1 1 1 0 10
1 0 0 1 0 0 11 1 1 0 1 0 0 10 0 0 1 0 0 1 10 1 0 0 1 0 1 11
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

30. SINGLE-POINT CROSSOVER
1 0 1 1 0 1 0 1
1 1 0 1 1 1 1 0X
0 1 0 1
1 1 0 1 1 1 1 0
1 0 1 1
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

31. SINGLE-POINT CROSSOVER
1 0 1 1 0 1 0 1
1 1 0 1 1 1 1 0X
0 1 0 1
1 1 0 1
1 1 1 01 0 1 1
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

32. UNIFORM CROSSOVER
1 0 1 1 0 1 0 1
1 1 0 1 1 1 1 0X
1 0 1
1 1 0 1 1 1 0
1 0 1 1 0
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

33. UNIFORM CROSSOVER
1 0 1 1 0 1 0 1
1 1 0 1 1 1 1 0X
1
0
1
1
1
0
1
1
1 01
0
1
1
0
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

34. COMPARISON
SINGLE POINT CROSSOVER UNIFORM CROSSOVER
num_items=64, population_size=10, iterations=15, elitism=0, seed=“Knapsack01”
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
Best Mean WorstBest Mean Worst

35. MEASURING PERFORMANCE
Solution fitness
Clock time to converge
Iterations to converge
Number of solutions explored
Inheritability
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

36. POINT MUTATION
1 0 0 1 0 1 1 00
1
X
1 0 0 1 1 1 0
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

37. 7. SOLUTION
SOLUTION
6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0
0 0 1 1 1 0 0 0 0 0 1 1 0 1 1 0

38. 7. SOLUTION
SOLUTION
6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
0 0 1 1 0 1 1 0
A B C D E F G H
10 lbs
$11
4 lbs
$8
5 lbs
$6
2 lbs
$12
8 lbs
$5
2 lbs
$8
6 lbs
$4
7 lbs
$13
WEIGHT: 15 lbs
VALUE: $30

39. SEATING CHART PROBLEM
What seating arrangement will:
1. Seat people of the same job
together?
2. Separate loud jobs from
quiet jobs?
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

40. SEATING CHART PROBLEM
ID Name Role Seat
0 Sandra Rodgers Engineer 0
1 Kirk Spence Engineer 3
2 Terry Burton Engineer 2
3 Cindy Hill Engineer 4
4 Steve Thompson Engineer 6
5 Laura Juel Engineer 5
6 Emily Cook Engineer C
7 Daniel Barrett Manager 8
8 Cedric White Manager 9
9 Emily Grimaud Sales E
10 Joseph Johnson Sales B
11 Raymond Seery Sales F
0 1
4
2 3
8
C
5 6 7
9 A B
D E F
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

41. PERMUTATION ENCODING
ID Name Role Seat
0 Sandra Rodgers Engineer 0
1 Kirk Spence Engineer 3
2 Terry Burton Engineer 2
3 Cindy Hill Engineer 4
4 Steve Thompson Engineer 6
5 Laura Juel Engineer 5
6 Emily Cook Engineer C
7 Daniel Barrett Manager 8
8 Cedric White Manager 9
9 Emily Grimaud Sales E
10 Joseph Johnson Sales B
11 Raymond Seery Sales F
0 1
4
2 3
8
C
5 6 7
9 A B
D E F
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

42. PERMUTATION ENCODING
0 12 3 45 67 8 910 11
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

43. PERMUTATION ENCODING
0 12 3 45 67 8 910 1112 13 14 15
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

44. NAÏVE VECTORIZATION
0 1
4
2 3
8
C
5 6 7
9 A B
D E F
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

45. HILBERT VECTORIZATION
0 1
3
E F
4
5
2 D C
7 8 B
6 9 A
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

46. PRESERVING LOCALITY
NAÏVE VECTORIZATION HILBERT VECTORIZATION
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 150 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
vector index
distancetoorigin
vector index
distancetoorigin

47. PERFORMANCE COMPARISON
NAÏVE VECTORIZATION HILBERT VECTORIZATION
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
map_size=7x7, population_size=200, iterations=30, elitism=0, seed=“SeatingChart”, crossover=PMX
Best Mean Worst Best Mean Worst

48. INITIALIZE THE POPULATION
2 6 0 1 4 7 3 5
1 7 5 0 4 2 3 6
7 3 1 0 2 6 5 4
7. SOLUTION6. MUTATION5. CROSSOVER4. FITNESS3. CREATION3. CREATION2. ENCODING1. PROBLEM 7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

49. FITNESS FUNCTION
0 1
4
2 3
C
5 6 7
A B
D E F
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
8 9

50. FITNESS FUNCTION
0 1
4
2 3
C
5 6 7
A B
D E F
44.03
3.41
1.00
48.44
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
8 9

51. FITNESS FUNCTION
0 1
4
2 3
8
C
5 6 7
9 A B
D E F
30.38
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

52. FITNESS FUNCTION
0 1
4
2 3
8
C
5 6 7
9 A B
D E F
-30.38
48.44
18.06
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

53. FITNESS FUNCTION
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
def score(self, chromosome):
seats_role = self.seats_by_role(chromosome)
# Tally attractive score
attraction = 0.0
for role, coords in seats_role.iteritems():
attraction += spatial.total_edge_length(coords)
# Tally repulsive score
repulsion = 0.0
for role, repulsors in self.repulsion.iteritems():
…
return self.max_distance - attraction + repulsion

54. ORDERED CROSSOVER (OX)
X
2 6 0 1 4 7 3 5
1 7 5 0 4 2 3 67 5 4 3
2 6 0 1
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

55. ORDERED CROSSOVER (OX)
X
2 6 0 1 4 7 3 5
1 7 5 0 4 2 3 67 5 4 3
2 6 0 1
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

56. ORDERED CROSSOVER (OX)
X
2 6 0 1 4 7 3 5
1 7 5 0 4 2 3 6
7 5 4 32 6 0 1
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

57. PARTIALLY MATCHED CROSSOVER (PMX)
X
2 6 0 1 4 7 3 5
0 1 5 7 42 3 6
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
5 7 2

58. PARTIALLY MATCHED CROSSOVER (PMX)
X
2 6 0 1 4 7 3 5
0 1 5 7 42 3 6
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
5 7 2

59. PARTIALLY MATCHED CROSSOVER (PMX)
X
2 6 0 1 4 7 3 5
0 1 5 7 42 3 6
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
5 7 2
0

60. PARTIALLY MATCHED CROSSOVER (PMX)
X
2 6 0 1 4 7 3 5
0 1 5 7 42 3 6
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
5 7 2 0

61. PARTIALLY MATCHED CROSSOVER (PMX)
X
2 6 0 1 4 7 3 5
0 1 5 7 42 3 6
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
5 7 2 0
1

62. PARTIALLY MATCHED CROSSOVER (PMX)
X
2 6 0 1 4 7 3 5
0 1 5 7 42 3 6
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
5 7 2 01

63. PARTIALLY MATCHED CROSSOVER (PMX)
X
2 6 0 1 4 7 3 5
0 1 5 7 42 3 6
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
5 7 2 01
4

64. PARTIALLY MATCHED CROSSOVER (PMX)
X
2 6 0 1 4 7 3 5
0 1 5 7 42 3 6
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
5 7 2 014

65. PARTIALLY MATCHED CROSSOVER (PMX)
X
2 6 0 1 4 7 3 5
0 1 5 7 42 3 6
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
5 7 2 014
6 3

66. PARTIALLY MATCHED CROSSOVER (PMX)
X
2 6 0 1 4 7 3 5
0 1 5 7 42 3 6
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM
5 7 2 014 6 3

67. PERFORMANCE COMPARISON
ORDERED PARTIALLY MATCHED
7. SOLUTION6. MUTATION5. CROSSOVER
map_size=7x7, population_size=200, iterations=30, elitism=0, seed=“SeatingChart”, map_type=naive
Best Mean Worst Best Mean Worst
4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

68. SWAP MUTATION
X 2 6 0 1 4 7 3 5
2 60 14 7 3 5
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

69. SOLUTION
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

70. SOLUTION
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

71. SOLUTION
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

72. SOLUTION
7. SOLUTION6. MUTATION5. CROSSOVER4. SELECTION3. FITNESS2. CREATION1. ENCODING0. PROBLEM

73. UNBOUNDED KNAPSACK PROBLEM
Given a set of items, each with a quantified
weight and value ($), what combination of
those items in any amounts will:
1. Fit inside a knapsack capable of carrying a
fixed amount of weight?
2. Maximize the value of the knapsack’s
payload?
15 lbs

74. UNBOUNDED KNAPSACK PROBLEM
A
10 lbs | $11
B
4 lbs | $8
C
5 lbs | $6
D
2 lbs | $12
20 lbs
E
8 lbs | $5
F
2 lbs | $8
G
6 lbs | $4
H
7 lbs | $13

75. LIST ENCODING
B D E F H
B D E F
A B D E HH
B D AC
E H A

76. UNBOUNDED KNAPSACK PROBLEM
Encoding List
Crossover Cut and Splice / Single Point
Selection Proportionate
Mutation Point

77. TRAVELING SALESMAN PROBLEM
Minimize the total distance
required to visit all stops along
a route.

78. TRAVELING SALESMAN PROBLEM
Encoding Permutation
Crossover Ordered or Edge Recombination
Selection Proportionate
Mutation Swap

79. NURSE SCHEDULING PROBLEM
There must always be n nurses
scheduled.
A nurse cannot be scheduled
while on PTO.
A nurse cannot be scheduled for
more than two consecutive shifts.
A nurse should have at least two
shifts off between scheduled
shifts.
A nurse’s shift preferences should
be respected.
7 days / week
3 shifts / day

80. NURSE SCHEDULING PROBLEM
Name Shift Mon Tue Wed Thu Fri Sat Sun
Grace 1
Constance 3 PTO PTO
Ronald 2
Robyn 2 PTO PTO
Judy 1
Amy 1, 2
Brad 3
Valarie 1 PTO
James 2
Thelma 2, 3 PTO
… … … … … … … … …

81. NURSE SCHEDULING PROBLEM
Encoding Binary (with constraints)
Crossover Single Point Crossover
Selection Proportionate
Mutation Point

82. ENCODING CONSTRAINTS
1 0
Grace
0
Mon
1 2 3
Tues
1 0
1 2 …
… 0 1
Constance
0
Mon
1 2 3
Thurs
1 0
1 2 3
1 …
…
…
…
…
…
… …Shift
Day

83. WHEN TO EVOLVE
Use a GA on problems when:
There may not be an exact solution
Search spaces are large
The fitness landscape is complex
The best solutions lie on a Pareto
front

84. GENETIC
ALGORITHMS
SIMULATED
ANNEALINGVS
Finds better solutions
Global search
Easily parallelized
Broader applications
Finds solutions faster
Local search

85. GENETIC
ALGORITHMS
BRANCH &
BOUNDVS
Approximate solutions
Better able to cope with
large, complex problems
Best solution, guaranteed
Limited to small problems
O(n log n)

86. GENETIC
ALGORITHMS
GRADIENT
DESCENTVS
Discrete optimization
Wins at complex fitness
landscapes
No calculus required =)
Continuous optimization
Wins at speed

87. GENETIC
ALGORITHMS
NEURAL
NETWORKSVS
Optimization
Classification (GBML)
Human Comparable Design
Classification†
Pattern recognition
† Also for screenplay creation

88. FURTHER READING

89. LIFE FINDS A WAY…
Special Thanks To:
Brook Graham
Dr. Wes Kerr
Kyle Putnam
Paul Sipe
Jennifer Wadella