HyperMu’NmGA

HyperMu’NmGA preview image

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Cosimo.leuci Cosimo Leuci (Author)

Tags

evolvability 

Tagged by Cosimo Leuci about 2 years ago

genetic algorithms 

Tagged by Cosimo Leuci almost 2 years ago

hypermutation 

Tagged by Cosimo Leuci about 2 years ago

sex 

Tagged by Cosimo Leuci almost 2 years ago

tumorigenesis 

Tagged by Cosimo Leuci almost 2 years ago

Child of model Minimal Genetic Algorithm preview imageMinimal Genetic Algorithm
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;;  _______________________________________________________________________________________________________________
;;
;;  --------------------   HyperMu'NmGA   ---------------------------------------------------------   HyperMu'NmGA
;;  HyperMu'NmGA   ---------------------------------------------------------   HyperMu'NmGA   ---------------------
;;  _______________________________________________________________________________________________________________



globals     [best_result   ;; the numerical value of the chromosome  closer to the solution
             worst_result  ;; the numerical value of the chromosome more distant from the solution
             eureka-time   ;; the ticks elapsed to find the optimal solution
             eureka!       ;; binary variable turning on when the optimal solution has just reached
             reduction-time;; the ticks required to get a 30% reduction of the mutator genes values average
             donor         ;; the turtle having the best chromosome
             recipient     ;; a random turtle different from the donor
             chromcopy     ;; the copy of the chromosome carried by the donor turtle
             mutcopy       ;; the copy of the mutaror gene carried by the donor turtle
             mate_swcopy   ;; the copy of the mate_switch gene carried by the donor turtle
             mutator_swcopy;; the copy of the mutator_switch gene carried by the donor turtle
             max-mut.av    ;; the highest mutators average value recorded before the problem solution
             min-mut.av    ;; the lowest mutators average value recorded before the problem solution
             a-split       ;; the position of one extreme in a donor's chromosome fragment
             b-split       ;; the position of the second extreme  a in a donor's chromosome fragment
             counter       ;; a counter usefull in different blocks
             ]

turtles-own [chromosome    ;; a string of digits representing a candidate solution to the problem
             mutator       ;; a gene able to impact positively or negatively on the genetic stability of the
                           ;; chromosome and of itself; its strength is quantified by a whole number
             mutator_switch;; a gene able to determine the mutation mode;
             mate_switch   ;; a gene able to determine the reproduction mode;
                           ;; it harbours a binary variable: 0 for asexual reproduction, 1 for sexual reproduction
             fitness       ;; the numerical value of the chromosomal strings (if string-value fitness-function mode)
                           ;; or the Hamming distance complement (i.e. the proximity) to the best chromosomal
                           ;; string (if hamming fitness-function mode)
             ]



;;  ----------   SETUP PROCEDURES   -------------------------------------------------------------------------------
;;  ---------------------------------------------------------------------------------------------------------------

to setup                  ;; reset parameters and create a number of turtles set by the user
  clear-all
  set counter 0
  reset-ticks
  create-turtles turtles-number [
    set shape "circle 2"
    set size 2.3
    fd random 15
    genotype/fenotype-construction
   ]
  message0
  set eureka-time 0
end 

to genotype/fenotype-construction
  ;; the genotype (chromosome) and the corresponding phenotype (fitness) of the turtle are initially equal to the
  ;; first answer that is usually given by children to the problem: "what is the greatest number of n digits?"
  set fitness 10 ^ (genes-number - 1)
  ;; the initial formal fitness value is converted into a string: it will be the chromosome structure
  set chromosome word fitness ""
  set label chromosome    ;; chromosome is displayed as label

  ;; in the "hamming" mode, the fitness function is replaced by the complement of the Hamming distance
  ;; (i.e. the "Hamming proximity") to the optimal solution
  if fitness-function = "hamming" [compute-fitness]

  set mutator 1
  set max-mut.av 1
  set min-mut.av 1

  ;; defining mate_switch gene
  if reproduction = "asexual" [set mate_switch 0]
  if reproduction = "sexual" [set mate_switch 1]
  if reproduction = "sex/asex" [set mate_switch random 2]

  ;; defining mutator_switch gene
  if mutate_by = "simple mutagenesis" [set mutator_switch -1]
  if mutate_by = "additive mutator" [set mutator_switch 0]
  if mutate_by = "multiplicative mutator" [set mutator_switch 1]
  if mutate_by = "both mutators" [set mutator_switch random 2]
end 


;;  ----------   RUNTIME PROCEDURES   -----------------------------------------------------------------------------
;;  ---------------------------------------------------------------------------------------------------------------

to search
  ask turtles [move]
  ;; thew worse and the best fitness value are detected
  set worst_result min [fitness] of turtles
  set best_result max [fitness] of turtles

  ;; a message is sent to output when the optimal solution is reached  (the solutions are differently detected
  ;; depending on the chosen fitness function)
  if eureka-time = 0 and (best_result = 10 ^ genes-number - 1 or best_result = genes-number) [
      set eureka-time ticks
      set eureka! 1
      message1
    if mutate_by = "simple mutagenesis" [stop]
    ]
  ;; max-mut.av and min-mut.av are recorded before the optimal solution is reached
  if eureka-time = 0  [
    if max-mut.av < mean [mutator] of turtles [set max-mut.av  mean [mutator] of turtles]
    if min-mut.av > mean [mutator] of turtles [set min-mut.av  mean [mutator] of turtles]
    ]
  ;; other STOP conditions
  if eureka-time > 0 [set reduction-time ticks - eureka-time]
  if eureka-time > 0 and mean [mutator] of turtles < 0.7 [message2 stop]
  if eureka-time = 0 and ticks >= 750000 and max [mutator] of turtles < 1 [message3 stop]
  if eureka-time > 0 and min [mutator] of turtles >= 1.5 and reduction-time > 500000  [message4 stop]

  ;; two alternative kinds of selective reproduction are admitted: sexual and asexual
  ;; they take place if the diversity between all chromosomes is not null
  clear-links
  if best_result != worst_result [
    set donor [who] of one-of turtles with [fitness = best_result]
    set chromcopy [chromosome] of turtle donor
    set mutcopy [mutator] of turtle donor
    set mate_swcopy [mate_switch] of turtle donor
    set mutator_swcopy [mutator_switch] of turtle donor
    set recipient [who] of one-of turtles with [fitness != best_result]
    ask turtle donor [
      create-link-to turtle recipient
      if mate_switch = 0 [cloning]
      if mate_switch = 1 [recombination]
    ]
  ]
  ;; mutations occur randomly, the frequency is related to the basic mutation-rate (the mutagenicity
  ;; of the environment) and the total gene number of turtles population
  if random-float 1 < basic_mutation-rate * turtles-number * (genes-number + 3)
     [ask one-of turtles [mutation]
  ]
  tick
  set eureka! 0
end 

;; TALK TO ME! ---------------------------------------------------------------------------------------------------

to message0
  output-print "*** HyperMu'NmGA ***"
  output-print " "
end 

to message1
  output-print " OPTIMAL SOLUTION"
  output-print " reached on "
  output-print word " tick: " eureka-time
end 

to message2
  output-print " "

  output-print " Mutators mean went"
  output-print " down 30% under the"
  output-print  " starting value after"
  output-type " ticks: "
  output-type reduction-time
end 

to message3
  output-print " "
  output-print " Low evolutive"
  output-print " potential"
end 

to message4
  output-print " "
  output-print " No mutator < 1.5"
  output-print " possible endless"
  output-print " hypermutation status"
end 

;; GOOD VIBRATIONS -----------------------------------------------------------------------------------------------

to-report  patches-ahead [rad dis] ; reports to turtles a set of patches ahead
  report [patches in-radius rad] of patch-ahead dis
end 

to move
   ifelse (any? other turtles-on patches-ahead 1 1)
    [ bk 1 lt random-float 360]
    [fd 0.001]
end 


;; Operator 1.  FITNESS FUNCTIONS
;; ----------------------------------------------------------------------------------------------------------------

to compute-fitness
  set fitness 0
  set counter 0
  if fitness-function = "hamming" [hamming-proximity]
  if fitness-function = "string-value" [
    set fitness read-from-string chromosome]
  set label chromosome
end 

to hamming-proximity
  if counter = length chromosome [set counter 0 stop]
  if item counter chromosome = "9" [
    set fitness fitness + 1
    ]
  set counter counter + 1
  hamming-proximity
end 


;; Operator 2.  REPRODUCTION
;; ----------------------------------------------------------------------------------------------------------------

to cloning
  ask turtle recipient [
    set chromosome chromcopy
    set mutator mutcopy
    set mutator_switch mutator_swcopy
    set mate_switch mate_swcopy
    compute-fitness
  ]
end 

to recombination
  ;; homologous recombination occurs between the chromosome of a randomly chosen turtle (recipient) and the
  ;; chromosome of (one of) the most performing one (donor) that offers a code's fragment of its chromosome
  ;; to the first turtle; the two involved turtles will be highlighted by a link

  set counter 0
  set a-split random genes-number
  set b-split random genes-number
  ask turtle recipient [
    hybridization
    compute-fitness]
end 

to hybridization
  ;; the two selected chromosomal strings give place to hybridization according to a mechanism
  ;; similar to the crossing-over following to bacterial conjugation; strings are looped,
  ;; as occur usually in bacterial chromosomes or plasmids
  ifelse a-split < b-split [set chromosome
            replace-item (a-split + counter) chromosome (item (a-split + counter) chromcopy)
            set counter (counter + 1)
            if counter < b-split - a-split [hybridization]]
       [if b-split < a-split [set chromosome
              replace-item ((a-split + counter) mod genes-number)
                  chromosome (item ((a-split + counter) mod genes-number) chromcopy)
              set counter (counter + 1)
              if counter < genes-number - a-split + b-split [hybridization]]
    ]
  ;; if required, the three donor's regulative genes are transferred into the recipient turtle
  if reproduction = "sex/asex" [set mate_switch 1]
  if mutate_by != "simple mutagenesis" [
    if random-float 1 < hym-ratio [set mutator mutcopy]
    if mutate_by = "both mutators" [if random-float 1 < 0.5 [
      set mutator_switch mutator_swcopy]
    ]
  ]
end 


;; Operator 3: MUTAGENESIS
;; ----------------------------------------------------------------------------------------------------------------

to mutation
  ;; simple mutagenesis doesn't involve mutator genes
  ifelse mutate_by = "simple mutagenesis" [point-mutation compute-fitness
    if reproduction = "sex/asex" [ ;; mutation of the reproduction mode
      if random-float 1 < 0.5
       [set mate_switch ((mate_switch + 1) mod 2)]
    ]
  ]
  ;; hypermutation can produce a mutation of a structural gene as well as a mutation on the mutator gene;
  ;; the last one determines the mutation number along the chromosome: it can be 0, 1 or more (multimutation)
  [if random-float 1 > hym-ratio [multimutation compute-fitness]
   if random-float 1 < hym-ratio [hypermutation]
   ;; when the population is reproductively heterogeneous, also the mate_switch gene can mutate
   if reproduction = "sex/asex" [
     if random-float 1 * mutator * mu-expressivity > hym-ratio [
       set mate_switch ((mate_switch + 1) mod 2)]  ;; mutation of the reproduction mode
    ]
   ;; when the population is mutationally heterogeneous, also the mutator_switch gene can mutate
   if mutate_by = "both mutators" [
    if random-float 1 * mutator * mu-expressivity > hym-ratio [
      set mutator_switch ((mutator_switch + 1) mod 2)]  ;; mutation of the mutator mode
    ]
  ]
end 

to point-mutation
  ;; point-mutations hit randomly only one gene
  set chromosome replace-item random genes-number chromosome word random 10 ""
end 

to multimutation
  ;; the multimutations imply different point-mutations in a unique mutation event
  ;;  the number of contemporary point-mutations is determined by the variable stored in the mutator-gene
  ifelse mutator < genes-number * 4
    [repeat mutator [point-mutation]]
    ;; if the mutator value is too high (more than four times the number of the chromosome genes) in order to avoid
    ;; overflow events, the number of point-mutations is cut to four times the number of chromosome genes
    [repeat genes-number * 4 [point-mutation]
  ]
end 

to hypermutation
  ;; mutator gene can mutate itself  in two alternative ways (additive or multiplicative, decided by
  ;; mutator_switch gene), the self-mutation effect can be modulated also by the mutator-expressivity parameter
  set mutator mutator + (random 3 - 1) * mutator ^ mutator_switch * mu-expressivity
end 

There are 7 versions of this model.

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Cosimo Leuci 2 months ago Rev. 1.2.1 Download this version
Cosimo Leuci 2 months ago Rev. 1.2.0 Download this version
Cosimo Leuci 2 months ago Rev. 1.0.3 Download this version
Cosimo Leuci 2 months ago Rev. 1.0.2 Download this version
Cosimo Leuci 10 months ago Rev. 1.0.1 Download this version
Cosimo Leuci almost 2 years ago Rev. 1.0.0 Download this version
Cosimo Leuci about 2 years ago Rev. 1.0 Download this version

Attached files

File Type Description Last updated
HyperMu'NmGA - ODD Protocol.pdf pdf The ODD Protocol 2 months ago, by Cosimo Leuci Download
HyperMu’NmGA.png preview HyperMu-Reflection about 2 years ago, by Cosimo Leuci Download

Parent: Minimal Genetic Algorithm

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