This talk will introduce into programming with evolutionary algorithms. We'll build and/or discover a small framework for creating genetic algorithms. Genetic algorithms are good to solve certain kind of algorithmic problems. The talk will also give a rough picture about situations where evolutionary programming might be the right solution. Especially np hard problems like the traveling sales man problem (TSP) or the rucksack problem are well known combinatoric problems (such as distributing vservers across a number of vserver-hosts).

1. Evolutionary
Algorithms
in Ruby
Julian Fischer
fischer@enterprise-rails.de
http://www.enterprise-rails.de

2. Introduction

3. Introduction
About me

4. About me
Julian Fischer
‣Twitter: http://www.twitter.com/railshoster
‣E-Mail: fischer@enterprise-rails.de

5. About me
Julian Fischer
‣Twitter: http://www.twitter.com/railshoster
‣E-Mail: fischer@enterprise-rails.de
‣ CEO of Avarteq GmbH

6. About me
Julian Fischer
‣Twitter: http://www.twitter.com/railshoster
‣E-Mail: fischer@enterprise-rails.de
‣ CEO of Avarteq GmbH
‣ Lecturer „Ruby on Rails“ @ HTWdS

7. About me
Julian Fischer
‣Twitter: http://www.twitter.com/railshoster
‣E-Mail: fischer@enterprise-rails.de
‣ CEO of Avarteq GmbH
‣ Lecturer „Ruby on Rails“ @ HTWdS
‣ Ruby und Ruby on Rails programmer

8. About me
Julian Fischer
‣Twitter: http://www.twitter.com/railshoster
‣E-Mail: fischer@enterprise-rails.de
‣ CEO of Avarteq GmbH
‣ Lecturer „Ruby on Rails“ @ HTWdS
‣ Ruby und Ruby on Rails programmer
‣ Entperise-Rails.de - Head of Hosting

9. Introduction
About Avarteq GmbH

10. About Avarteq GmbH

11. About Avarteq GmbH
‣ Founded in Nov. 2008
from two existing companies.

12. About Avarteq GmbH
‣ Founded in Nov. 2008
from two existing companies.
‣ Involvment of Key-Systems GmbH
manages ~2,5 * 10^6 domains for customers of 200+ countries.

13. About Avarteq GmbH
‣ Founded in Nov. 2008
from two existing companies.
‣ Involvment of Key-Systems GmbH
manages ~2,5 * 10^6 domains for customers of 200+ countries.
‣ Team size: 14 people
8 full-time, 6 part-time/freelancer

14. Introduction
Portfolio

15. About Avarteq GmbH

16. About Avarteq GmbH
‣ Covers all stages of web development

17. About Avarteq GmbH
‣ Covers all stages of web development
‣ Consulting

18. About Avarteq GmbH
‣ Covers all stages of web development
‣ Consulting
‣ Conceptual ~ and screen design

19. About Avarteq GmbH
‣ Covers all stages of web development
‣ Consulting
‣ Conceptual ~ and screen design
‣ Ruby&onplace. development.
In house in
Rails

20. About Avarteq GmbH
‣ Covers all stages of web development
‣ Consulting
‣ Conceptual ~ and screen design
‣ Ruby&onplace. development.
In house in
Rails
‣ Ruby on Rails
hosting/servers/clusters
RailsHoster.de - Enterprise-Rails.de

21. Evolution

22. Evolution

23. Evolution

24. Evolution
‣ Population of individuals (organisms)

25. Evolution
‣ Population of individuals (organisms)
‣ Genes are passed from generation to
generation.

26. Evolution
‣ Population of individuals (organisms)
‣ Genes are passed from generation to
generation.
‣ Slightgeneration to thein genetic material
from one
changes
next.

27. Evolution
‣ Population of individuals (organisms)
‣ Genes are passed from generation to
generation.
‣ Slightgeneration to thein genetic material
from one
changes
next.
‣ Differences accumulate over time.

28. Evolution
‣ Population of individuals (organisms)
‣ Genes are passed from generation to
generation.
‣ Slightgeneration to thein genetic material
from one
changes
next.
‣ Differences accumulate over time.
‣ New properties or even species emerge.

29. Evolution
Genetic Operations

30. Evolution

31. Evolution
‣ Basic genetic operations

32. Evolution
‣ Basic genetic operations
‣ Mutation of genetic material.
Random changes

33. Evolution
‣ Basic genetic operations
‣ Mutation of genetic material.
Random changes
‣ Recombination of two individuals.
Recombine genetic material

34. Evolution
‣ Basic genetic operations
‣ Mutation of genetic material.
Random changes
‣ Recombination of two individuals.
Recombine genetic material
‣ also known as sexual reproduction

35. Evolution

36. Evolution
‣ Selection

37. Evolution
‣ Selection
‣ Who will survive and thus

38. Evolution
‣ Selection
‣ Who will survive and thus
‣ is able to create the most offsprings?

39. Evolution Challenges

40. Evolution

41. Evolution
‣ Changing environment and a lot of other changing
temperature, energy resources, competitors
and moving stuff, ...

42. Evolution
‣ Changing environment and a lot of other changing
temperature, energy resources, competitors
and moving stuff, ...
‣ Huge search space countless possible individuals.
vast amount of dna combinations,

43. Evolution
‣ Changing environment and a lot of other changing
temperature, energy resources, competitors
and moving stuff, ...
‣ Huge search space countless possible individuals.
vast amount of dna combinations,
‣ Restricted resources
Death = release of allocated resources.

44. Evolution

45. Evolution
‣ Which combinations to create as an
individual?

46. Evolution
‣ Which combinations to create as an
individual?
‣ Only validthey usually don‘t breed a banana plant but another
If two dogs breed
individuals
dog, instead!

47. Evolution
‣ Which combinations to create as an
individual?
‣ Only validthey usually don‘t breed a banana plant but another
If two dogs breed
individuals
dog, instead!
‣ Preserve good gene materialetc. have a lot of
Maybe that‘s why rockstars, football champions,
girlfriends?! Hey, I am a Ruby programmer, that‘s also awesome!

48. Evolution
‣ Which combinations to create as an
individual?
‣ Only validthey usually don‘t breed a banana plant but another
If two dogs breed
individuals
dog, instead!
‣ Preserve good gene materialetc. have a lot of
Maybe that‘s why rockstars, football champions,
girlfriends?! Hey, I am a Ruby programmer, that‘s also awesome!
‣ Improve! but even improve what‘s good and make it better!
Not only preserve

49. How does that relate to
computer science and
Ruby?

50. Evolution and Ruby?

51. Evolution and Ruby?
Given:
Search or optimization problem with a
huge search space

52. Evolution and Ruby?
Given:
Search or optimization problem with a
huge search space
Wanted:
A (heuristic) solution.

53. Evolutionary
Programming
(EP) ...

54. ...with
Genetic Algorithms (GA)

55. Genetic Algorithms
Basic Idea

56. Imitate the
evolutionary process.

57. Genetic Algorithms
What we need

58. Genetic Algorithms

59. Genetic Algorithms
‣ Generation = Nr. of individuals

60. Genetic Algorithms
‣ Generation = Nr. of individuals
‣ Individualbut a valid problem solution
maybe a bad one
=
valid.

61. Genetic Algorithms
‣ Generation = Nr. of individuals
‣ Individualbut a valid problem solution
maybe a bad one
=
valid.
‣ Individuals constist of genes

62. Genetic Algorithms
‣ Generation = Nr. of individuals
‣ Individualbut a valid problem solution
maybe a bad one
=
valid.
‣ Individuals constist of genes
‣ Individuals havethe individual (good .. badfitness
indicating the worthiness of
a well known
solution).

63. Genetic Algorithms

64. Genetic Algorithms
‣ Mutation operation

65. Genetic Algorithms
‣ Mutation operation
‣ Recombination operation

66. Genetic Algorithms
‣ Mutation operation
‣ Recombination operation
‣ Selection operation

67. Genetic Algorithms
Evolutionary Process

68. Genetic Algorithms

69. Genetic Algorithms
‣ Start with a first individual (genesis)

70. Genetic Algorithms
‣ Start with a first individual (genesis)
‣ Create 2nd individual through mutation

71. Genetic Algorithms
‣ Start with a first individual (genesis)
‣ Create 2nd individual through mutation
‣ Randomly/Statistically choose a genetic
operation to create new individuals

72. Genetic Algorithms

73. Genetic Algorithms
‣ If a generation is full create a new one

74. Genetic Algorithms
‣ If a generation is full create a new one
‣ Select fittest individuals to be passed in to
the next generation.

75. Genetic Algorithms
‣ If a generation is full create a new one
‣ Select fittest individuals to be passed in to
the next generation.
‣ Repeat the process until we have an
acceptable solution or hit a given
boundary.

76. Concrete?!

77. Genetic Algorithms
Example

78. Travelling Salesman
Problem (TSP)

79. Traveling Salesman

80. Traveling Salesman
‣ A salesman needs to travel to n cities

81. Traveling Salesman
‣ A salesman needs to travel to n cities
‣ Each city needs to be visited once

82. Traveling Salesman
‣ A salesman needs to travel to n cities
‣ Each city needs to be visited once
‣ The salesman wants to end where he
started.

83. Traveling Salesman
‣ A salesman needs to travel to n cities
‣ Each city needs to be visited once
‣ The salesman wants to end where he
started.
‣ The route should be minimal.

84. Why should we care about
salesmen?

85. Traveling Salesman

86. Traveling Salesman
‣ TSP can be applied to many scenarios:

87. Traveling Salesman
‣ TSP can be applied to many scenarios:
‣ (Flight) planning, logistics, ...

88. Traveling Salesman
‣ TSP can be applied to many scenarios:
‣ (Flight) planning, logistics, ...
‣ Manufactoring of microchips

89. Traveling Salesman
‣ TSP can be applied to many scenarios:
‣ (Flight) planning, logistics, ...
‣ Manufactoring of microchips
‣ Slightly modified in DNA sequencing

90. Traveling Salesman
‣ TSP can be applied to many scenarios:
‣ (Flight) planning, logistics, ...
‣ Manufactoring of microchips
‣ Slightly modified in DNA sequencing
‣ ...

91. Traveling Salesman

92. Traveling Salesman
‣ What‘s the deal about it?

93. Traveling Salesman
‣ What‘s the deal about it?
‣ Problem of combinatorical optimization

94. Traveling Salesman
‣ What‘s the deal about it?
‣ Problem of combinatorical optimization
‣ Not a shortest path problem!

95. Traveling Salesman
‣ What‘s the deal about it?
‣ Problem of combinatorical optimization
‣ Not a shortest path problem!
‣ Computational complexity:
np complete
nondeterministic polynomial time

96. Too many possible
combinations!

97. Traveling Salesman

98. Traveling Salesman
‣ Most direct solution: Check permutations

99. Traveling Salesman
‣ Most direct solution: Check permutations
‣ O(n!)

100. Traveling Salesman
‣ Most direct solution: Check permutations
‣ O(n!)
‣ 5! = 120 combinations

101. Traveling Salesman
‣ Most direct solution: Check permutations
‣ O(n!)
‣ 5! = 120 combinations
‣ 10! = 3.628.800 combinations

102. Traveling Salesman
‣ Most direct solution: Check permutations
‣ O(n!)
‣ 5! = 120 combinations
‣ 10! = 3.628.800 combinations
‣ 15! = 1.307.674.368.000
combinations

103. Traveling Salesman
‣ Most direct solution: Check permutations
‣ O(n!)
‣ 5! = 120 combinations
‣ 10! = 3.628.800 combinations
‣ 15! = 1.307.674.368.000
combinations
‣ 20! ~ 2,43 * 10^18 combinations

104. Genetic Algorithms
TSP

105. Use a genetic algorithm to find a
heuristic solution

106. TSP
Individual

107. TSP Individual

108. TSP Individual
‣ Valid roundtrip- = cycle of -cities Saarbrücken.
Saarbrücken - Amsterdam Madrid - Poznan Berlin -

109. TSP Individual
‣ Valid roundtrip- = cycle of -cities Saarbrücken.
Saarbrücken - Amsterdam Madrid - Poznan Berlin -
‣ Individual = valid roundtrip

110. TSP Individual
‣ Valid roundtrip- = cycle of -cities Saarbrücken.
Saarbrücken - Amsterdam Madrid - Poznan Berlin -
‣ Individual = valid roundtrip
‣ Genes = cities

111. TSP
Fitness

112. TSP Fitness

113. TSP Fitness
‣ Each city knows its geo coords (lat, lon)

114. TSP Fitness
‣ Each city knows its geo coords (lat, lon)
‣ Calculate distance between two geo
coords

115. TSP Fitness
‣ Each city knows its geo coords (lat, lon)
‣ Calculate distance between two geo
coords
‣ Fitness = sum of distances between cities

116. TSP
Genetic Operations

117. Genetic Operations
Mutation

118. TSP Fitness

119. TSP Fitness
‣ Shuffle mutation

120. TSP Fitness
‣ Shuffle mutation
‣ Completely shuffle the order of cities in
the roundtrip

121. TSP Fitness
‣ Shuffle mutation
‣ Completely shuffle the order of cities in
the roundtrip
‣ 1-3-5-4-2 → 3-5-1-2-4

122. TSP Fitness
‣ Shuffle mutation
‣ Completely shuffle the order of cities in
the roundtrip
‣ 1-3-5-4-2 → 3-5-1-2-4
‣ (-) Doesn‘t rely on existing individuals

123. TSP Fitness
‣ Shuffle mutation
‣ Completely shuffle the order of cities in
the roundtrip
‣ 1-3-5-4-2 → 3-5-1-2-4
‣ (-) Doesn‘t rely on existing individuals
‣ Not a slightly but a massive change

124. TSP Fitness

125. TSP Fitness
‣ Partly shuffle

126. TSP Fitness
‣ Partly shuffle
‣ Exchange two randomly choosen cities

127. TSP Fitness
‣ Partly shuffle
‣ Exchange two randomly choosen cities
‣ 1-3-5-4-2 → 1-3-4-5-2

128. TSP Fitness
‣ Partly shuffle
‣ Exchange two randomly choosen cities
‣ 1-3-5-4-2 → 1-3-4-5-2
‣ (+) Uses existing individuals

129. TSP Fitness
‣ Partly shuffle
‣ Exchange two randomly choosen cities
‣ 1-3-5-4-2 → 1-3-4-5-2
‣ (+) Uses existing individuals
‣ Many more mutation operations
imaginable.

130. Genetic Operations
Recombination

131. TSP Recombination

132. TSP Recombination
‣ Recombination challenge:

133. TSP Recombination
‣ Recombination challenge:
‣ How to combine parts of individuals in
a way that a valid roundtrip will be
produced?

134. Recombination
Cycle Crossover

135. Cycle Crossover

136. Cycle Crossover
‣ Pass randomly choosen parts from the
the parents to the offsprings

137. Cycle Crossover
‣ Pass randomly choosen parts from the
the parents to the offsprings
‣ Creates two offsprings

138. Cycle Crossover
‣ Pass randomly choosen parts from the
the parents to the offsprings
‣ Creates two offsprings
‣ Example

139. Cycle Crossover
‣ Pass randomly choosen parts from the
the parents to the offsprings
‣ Creates two offsprings
‣ Example
‣ mom = [2, 3, 5, 7, 1, 6, 4]

140. Cycle Crossover
‣ Pass randomly choosen parts from the
the parents to the offsprings
‣ Creates two offsprings
‣ Example
‣ mom = [2, 3, 5, 7, 1, 6, 4]
‣ dad = [4, 5, 6, 3, 2, 7, 1]

141. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]

142. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Choose a gene from the first parent
(e.g. gene=3, index=1).

143. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Choose a gene from the first parent
(e.g. gene=3, index=1).
‣ Add it to the cycle.

144. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Choose a gene from the first parent
(e.g. gene=3, index=1).
‣ Add it to the cycle.
‣ Cycle: [3]

145. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]

146. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for gene with the same index (1) in
Dad's genes (gene=5, index=1).

147. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for gene with the same index (1) in
Dad's genes (gene=5, index=1).
‣ Add it to the cycle.

148. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for gene with the same index (1) in
Dad's genes (gene=5, index=1).
‣ Add it to the cycle.
‣ Cycle: [3, 5]

149. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]

150. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for the corresponding gene
(gene=5, index=2) in Mom's genes.

151. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for the corresponding gene
(gene=5, index=2) in Mom's genes.
‣ Look for the gene at index 2 at Dad's
genes (gene=6, index=2).

152. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for the corresponding gene
(gene=5, index=2) in Mom's genes.
‣ Look for the gene at index 2 at Dad's
genes (gene=6, index=2).
‣ Add it to the cycle.

153. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for the corresponding gene
(gene=5, index=2) in Mom's genes.
‣ Look for the gene at index 2 at Dad's
genes (gene=6, index=2).
‣ Add it to the cycle.
‣ Cycle: [3, 5, 6]

154. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]

155. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for the corresponding gene in
Dad's genes (gene=6, index=5).

156. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for the corresponding gene in
Dad's genes (gene=6, index=5).
‣ Look for the gene at index 5 (gene=7,
index=5) in Mom‘s genes.

157. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for the corresponding gene in
Dad's genes (gene=6, index=5).
‣ Look for the gene at index 5 (gene=7,
index=5) in Mom‘s genes.
‣ Add it to the cycle.

158. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for the corresponding gene in
Dad's genes (gene=6, index=5).
‣ Look for the gene at index 5 (gene=7,
index=5) in Mom‘s genes.
‣ Add it to the cycle.
‣ Cycle: [3, 5, 6, 7]

159. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]

160. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for the corresponding gene in
Moms's genes (gene=7, index=3).

161. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for the corresponding gene in
Moms's genes (gene=7, index=3).
‣ Look for the gene at index 3 (gene=3,
index=3).

162. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for the corresponding gene in
Moms's genes (gene=7, index=3).
‣ Look for the gene at index 3 (gene=3,
index=3).
‣ Add it to the cycle.

163. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
‣ Search for the corresponding gene in
Moms's genes (gene=7, index=3).
‣ Look for the gene at index 3 (gene=3,
index=3).
‣ Add it to the cycle.
‣ Cycle: [3, 5, 6, 7, 3]

164. Cycle Crossover
cycle = [3, 5, 6, 7, 3]

165. Cycle Crossover
cycle = [3, 5, 6, 7, 3]
‣ Now the cycle is complete!

166. Cycle Crossover
cycle = [3, 5, 6, 7, 3]
‣ Now the cycle is complete!
‣ We can create the offsprings.

167. Cycle Crossover
cycle = [3, 5, 6, 7, 3]
‣ Now the cycle is complete!
‣ We can create the offsprings.
‣ Genes which are in the cycle will be taken
from their parents preserving their original positions

168. Cycle Crossover
cycle = [3, 5, 6, 7, 3]
‣ Now the cycle is complete!
‣ We can create the offsprings.
‣ Genes which are in the cycle will be taken
from their parents preserving their original positions
moms_child = [x, 3, 5, 7, x, 6, x]
dads_child = [x, 5, 6, 3, x, 7, x]

169. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [x, 3, 5, 7, x, 6, x]
dads_child = [x, 5, 6, 3, x, 7, x]

170. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [x, 3, 5, 7, x, 6, x]
dads_child = [x, 5, 6, 3, x, 7, x]
‣ Missing genes will be taken from the
other parent.

171. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [x, 3, 5, 7, x, 6, x]
dads_child = [x, 5, 6, 3, x, 7, x]
‣ Missing genes will be taken from the
other parent.
mom = [2, 3, 5, 7, 1, 6, 4]
dads_child = [2, 5, 6, 3, 1, 7, 4]

172. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [x, 3, 5, 7, x, 6, x]
dads_child = [x, 5, 6, 3, x, 7, x]
‣ Missing genes will be taken from the
other parent.
mom = [2, 3, 5, 7, 1, 6, 4]
dads_child = [2, 5, 6, 3, 1, 7, 4]

173. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [x, 3, 5, 7, x, 6, x]
dads_child = [x, 5, 6, 3, x, 7, x]
‣ Missing genes will be taken from the
other parent.
mom = [2, 3, 5, 7, 1, 6, 4]
dads_child = [2, 5, 6, 3, 1, 7, 4]

174. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [x, 3, 5, 7, x, 6, x]
dads_child = [x, 5, 6, 3, x, 7, x]
‣ Missing genes will be taken from the
other parent.
mom = [2, 3, 5, 7, 1, 6, 4]
dads_child = [2, 5, 6, 3, 1, 7, 4]

175. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [x, 3, 5, 7, x, 6, x]
dads_child = [x, 5, 6, 3, x, 7, x]
‣ Missing genes will be taken from the
other parent.
mom = [2, 3, 5, 7, 1, 6, 4]
dads_child = [2, 5, 6, 3, 1, 7, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [4, 3, 5, 7, 2, 6, 1]

176. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [x, 3, 5, 7, x, 6, x]
dads_child = [x, 5, 6, 3, x, 7, x]
‣ Missing genes will be taken from the
other parent.
mom = [2, 3, 5, 7, 1, 6, 4]
dads_child = [2, 5, 6, 3, 1, 7, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [4, 3, 5, 7, 2, 6, 1]

177. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [x, 3, 5, 7, x, 6, x]
dads_child = [x, 5, 6, 3, x, 7, x]
‣ Missing genes will be taken from the
other parent.
mom = [2, 3, 5, 7, 1, 6, 4]
dads_child = [2, 5, 6, 3, 1, 7, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [4, 3, 5, 7, 2, 6, 1]

178. Cycle Crossover
mom = [2, 3, 5, 7, 1, 6, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [x, 3, 5, 7, x, 6, x]
dads_child = [x, 5, 6, 3, x, 7, x]
‣ Missing genes will be taken from the
other parent.
mom = [2, 3, 5, 7, 1, 6, 4]
dads_child = [2, 5, 6, 3, 1, 7, 4]
dad = [4, 5, 6, 3, 2, 7, 1]
moms_child = [4, 3, 5, 7, 2, 6, 1]

179. TSP
Evolver

180. Setup the
evolutionary process

181. TSP Evolver

182. TSP Evolver
‣ Generation size

183. TSP Evolver
‣ Generation size
‣ Choose genetic operations

184. TSP Evolver
‣ Generation size
‣ Choose genetic operations
‣ Probabilities of genetic operations

185. TSP Evolver
‣ Generation size
‣ Choose genetic operations
‣ Probabilities of genetic operations
‣ e.g. 30% mutation, 70% x-over

186. TSP Evolver
‣ Generation size
‣ Choose genetic operations
‣ Probabilities of genetic operations
‣ e.g. 30% mutation, 70% x-over
‣ Set exit condition

187. TSP Evolver
‣ Generation size
‣ Choose genetic operations
‣ Probabilities of genetic operations
‣ e.g. 30% mutation, 70% x-over
‣ Set exit condition
‣ ...

188. Go!

189. Code and Demo!

190. GA Challenges

191. Genetic Algorithms

192. Genetic Algorithms
‣ How to represent an individual?

193. Genetic Algorithms
‣ How to represent an individual?
‣ Find a meaningful fitness function

194. Genetic Algorithms
‣ How to represent an individual?
‣ Find a meaningful fitness function
‣ Can‘t be applied to boolean fitness
functions (match, no match)

195. Genetic Algorithms
‣ How to represent an individual?
‣ Find a meaningful fitness function
‣ Can‘t be applied to boolean fitness
functions (match, no match)
‣ Define than blind guessing skip it! operations
If it‘s worse
efficient genetic

196. Genetic Algorithms

197. Genetic Algorithms
‣ Don‘t get lost in the metaphor! a problem!
It‘s not about re-modeling the world, it‘s about solving

198. Genetic Algorithms
‣ Don‘t get lost in the metaphor! a problem!
It‘s not about re-modeling the world, it‘s about solving
‣ Non-determinism
1st run might find a very good solution, 2nd a totally bad one, ...

199. Genetic Algorithms
‣ Don‘t get lost in the metaphor! a problem!
It‘s not about re-modeling the world, it‘s about solving
‣ Non-determinism
1st run might find a very good solution, 2nd a totally bad one, ...
‣ Store your best solutions and use them
as the origin for the next run!

200. What is it good
for again?

201. Genetic Algorithms

202. Genetic Algorithms
‣ Find good solutions for np hard
combinatorical problems

203. Genetic Algorithms
‣ Find good solutions for np hard
combinatorical problems
‣ ... even for difficult search spaces!

204. What else can be done?

205. Genetic Algorithms

206. Genetic Algorithms
‣ Find smart break conditions

207. Genetic Algorithms
‣ Find smart break conditions
‣ e.g. exit if there is no change for n
generations

208. Genetic Algorithms
‣ Find smart break conditions
‣ e.g. exit if there is no change for n
generations
‣ Implement more genetic operations and
evaluate them.

209. Genetic Algorithms

210. Genetic Algorithms
‣ Optimize ratio between

211. Genetic Algorithms
‣ Optimize ratio between
‣ Generation size

212. Genetic Algorithms
‣ Optimize ratio between
‣ Generation size
‣ Operation propabilities

213. Genetic Algorithms
‣ Optimize ratio between
‣ Generation size
‣ Operation propabilities
‣ Fasten your fitness function!

214. Genetic Algorithms
‣ Optimize ratio between
‣ Generation size
‣ Operation propabilities
‣ Fasten your fitness function!
‣ Use heuristic fitness functions
estimate fitness to be faster

215. Genetic Algorithms
‣ Optimize ratio between
‣ Generation size
‣ Operation propabilities
‣ Fasten your fitness function!
‣ Use heuristic fitness functions
estimate fitness to be faster
‣ Cache frequently computed values
e.g. distances between cities

216. FAQ

217. FAQ

218. FAQ
‣ Do you use EP/GA for your everyday
work?

219. FAQ
‣ Do you use EP/GA for your everyday
work?
‣ Not very often ...

220. FAQ
‣ Do you use EP/GA for your everyday
work?
‣ Not very often ...
‣ ... but how often do you encounter np-
hard problems in everyday‘s work?

221. FAQ

222. FAQ
‣ Why talk about it?

223. FAQ
‣ Why talk about it?
‣ Interesting and generic methodology

224. FAQ
‣ Why talk about it?
‣ Interesting and generic methodology
‣ Solves NP hard problems heuristically
with minimal thought.

225. FAQ
‣ Why talk about it?
‣ Interesting and generic methodology
‣ Solves NP hard problems heuristically
with minimal thought.
‣ Gives a good understanding why some
people are what they are:
Just bad guesses :-)

226. Further questions?

227. Thank you
for your
attention!
Headquarter: Rails Enterprise Hosting:
http://www.avarteq.de http://www.enterprise-
rails.de
Blog:
http://ww.treibstofff.de Rails Hosting:
http://www.railshoster.de

228. Resources

229. Resources
‣ http://en.wikipedia.org/wiki/Travelling_salesman_problem

230. Resources
‣ http://en.wikipedia.org/wiki/Travelling_salesman_problem
‣ http://en.wikipedia.org/wiki/Evolution

231. Resources
‣ http://en.wikipedia.org/wiki/Travelling_salesman_problem
‣ http://en.wikipedia.org/wiki/Evolution
‣ http://en.wikipedia.org/wiki/Genetic_algorithm