Evolutionary Algorithms In Ruby

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    Notes on slide 1

    99,9% of all species(!) ever existed died out. The other 0,1% is about to do the same. But image if all those guys would still exist. Would be a fairly uncomfortable crowd, wouldn‘t it?

    99,9% of all species(!) ever existed died out. The other 0,1% is about to do the same. But image if all those guys would still exist. Would be a fairly uncomfortable crowd, wouldn‘t it?

    99,9% of all species(!) ever existed died out. The other 0,1% is about to do the same. But image if all those guys would still exist. Would be a fairly uncomfortable crowd, wouldn‘t it?

    acceptable = certain fitness,
    boundary = n generations, best individual didnt improve for n generations
    steady improvement of the fittest individuals

    acceptable = certain fitness,
    boundary = n generations, best individual didnt improve for n generations
    steady improvement of the fittest individuals

    acceptable = certain fitness,
    boundary = n generations, best individual didnt improve for n generations
    steady improvement of the fittest individuals

    What?

    What?

    What?

    What?

    You can‘t just try them all!

    How to determine the fitness of a tsp individual?

    How to determine the fitness of a tsp individual?

    How to determine the fitness of a tsp individual?

    How do we create individuals?

    Problem should be visualized!!!

    Problem should be visualized!!!

    You can‘t just try them all!

    You can‘t just try them all!

    1 Favorite

    Evolutionary Algorithms In Ruby - Presentation Transcript

    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
    SlideShare Zeitgeist 2009

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