Evolving Fractal Trees

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Tags

almost as cool as ifs kits 

"but only almost"

Tagged by Michelle Wilkerson-Jerde about 10 years ago

coolest models 

"because I need to compete with Michelle"

Tagged by Forrest Stonedahl about 10 years ago

fractals 

"fractals are fun"

Tagged by Reuven M. Lerner about 10 years ago

work-in-progress 

"should be self explanatory..."

Tagged by Forrest Stonedahl about 10 years ago

Model group Fractal Lovers | Visible to everyone | Changeable by everyone
Model was written in NetLogo 4.1beta2pre2 • Viewed 587 times • Downloaded 30 times • Run 15 times
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VERSION

$Id: Evolving Fractal Trees.nlogo 43333 2009-03-14 05:20:52Z fjs750 $

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NETLOGO FEATURES

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Click to Run Model

breed [drawings drawing ]

drawings-own [
   genome
   fitness
]

to setup
  ca
  ask patches [
    sprout-drawings 1 [
      set genome n-values 8 [ random-float 1.0 ]
      ht
    ]
  ]

 ask drawings [ do-drawing ]
end 

to go
  every 5 [
    ask drawings [ pen-up ]
    create-next-generation
    clear-drawing
    ask drawings
    [
      pen-down
      do-drawing
    ]
    tick
   ]
  if mouse-down? [
    ask patch mouse-xcor mouse-ycor [
      ask turtles-here [
        set fitness fitness + 1
      ]
    ]
  ]
end 

to do-drawing
  let max-dist 0.5
  let scale-factor 0.5 + 0.5 * item 0 genome
  let n floor (2 + 9 * item 1 genome)
  let angle1 floor (360 * item 2 genome)
  let angle2 floor (360 * item 3 genome)
  let col 1 + 10 * (floor 14 * item 4 genome)
  let col-incr 0.00 + 14 * item 5 genome
  let psize 5 * item 6 genome
  set heading 360 * item 7 genome

  let dist max-dist * (scale-factor - 1) / (scale-factor ^ n - 1)

  pd
  draw-tree n dist psize scale-factor col col-incr angle1 angle2
end 

to draw-tree [ n dist pensize scale-factor col col-incr angle1 angle2]
  if (n = 0) [ stop ]
  set pen-size pensize
  set color col
  fd dist
  rt angle1
  draw-tree (n - 1) dist * scale-factor pensize * scale-factor scale-factor col + col-incr col-incr angle1 angle2
  lt angle1
  rt angle2
  draw-tree (n - 1) dist * scale-factor pensize * scale-factor scale-factor col + col-incr col-incr angle1 angle2
  lt angle2
  set pen-size pensize
  set color col
  bk dist
end 

;; This procedure does the main work of the genetic algorithm.
;; We start with the old generation of solutions.
;; We choose solutions with good fitness to produce offspring
;; through crossover (sexual recombination), and to be cloned
;; (asexual reproduction) into the next generation.
;; There is also a chance of mutation occurring in each individual.
;; After a full new generation of solutions has been created,
;; the old generation dies.

to create-next-generation
  ; The following line of code looks a bit odd, so we'll explain it.
  ; if we simply wrote "LET OLD-GENERATION SONGS",
  ; then OLD-GENERATION would mean the set of all songs, and when
  ; new solutions were created, they would be added to the breed, and
  ; OLD-GENERATION would also grow.  Since we don't want it to grow,
  ; we instead write "SONGS WITH [TRUE]", which makes OLD-GENERATION
  ; an agentset, which doesn't get updated when new solutions are created.
  let old-generation drawings with [true]
  let population-size (count patches)
  ; Some number of the population is created by crossover each generation
  ; we divide by 2 because each time through the loop we create two children.
  let crossover-count  (floor (population-size * crossover-rate / 100 / 2))

  repeat crossover-count
  [
    ; We use "tournament selection", with tournament size = 3.
    ; This means, we randomly pick 3 solutions from the previous generation
    ; and select the best one of those 3 to reproduce.

    let parent1 min-one-of (n-of 3 old-generation) [fitness]
    let parent2 min-one-of (n-of 3 old-generation) [fitness]
    let inherited-fitness 0.3 * ([fitness] of parent1 + [fitness] of parent2)
    let child-genome-pair crossover ([genome] of parent1) ([genome] of parent2)

    ; create the two children, with their new genetic material
    ask parent1 [ hatch 1 [ set genome item 0 child-genome-pair set fitness inherited-fitness ] ]
    ask parent2 [ hatch 1 [ set genome item 1 child-genome-pair set fitness inherited-fitness ] ]
  ]

  ; the remainder of the population is created by cloning
  ; selected members of the previous generation
  repeat (population-size - crossover-count * 2)
  [
    ask max-one-of (n-of 3 old-generation) [fitness]
      [ hatch 1 [ set fitness 0.3 * fitness ] ]
  ]

  ask old-generation [ die ]

  ; now we're just talking to the new generation of solutions here
  ask drawings
  [
    ; there's a chance of mutations occurring
    mutate
    let emptyspots patches with [ not any? turtles-here ]
    if any? emptyspots [ move-to one-of emptyspots ]
  ]
end 

;; ===== Mutations

;; This reporter performs one-point crossover on two lists of bits.
;; That is, it chooses a random location for a splitting point.
;; Then it reports two new lists, using that splitting point,
;; by combining the first part of bits1 with the second part of bits2
;; and the first part of bits2 with the second part of bits1;
;; it puts together the first part of one list with the second part of
;; the other.

to-report crossover [bits1 bits2]
  let split-point 1 + random (length bits1 - 1)
  report list (sentence (sublist bits1 0 split-point)
                        (sublist bits2 split-point length bits2))
              (sentence (sublist bits2 0 split-point)
                        (sublist bits1 split-point length bits1))
end 

to mutate   ;; song procedure
  set genome map [ifelse-value (random-float 100.0 < mutation-rate) [(? + random-normal 0 0.5) mod 1.0] [?]]
               genome
end 

There are 2 versions of this model.

Uploaded by When Description Download
Forrest Stonedahl almost 9 years ago I moved around interface items. I didn't do anything else. I don't know where this model is going. this is just a test, to see how long of comments I can make. Download this version
Forrest Stonedahl almost 9 years ago Initial upload Download this version

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