Xavier Nayrac

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Un algorithme génétique en Julia - partie 10

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Niveau : intermédiaire

Voici le nouveau code de l’algorithme:

main.jl
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type Chromosome
  genes
end

create_genes(size) = rand(0:1, size)

function create_population(size, chromosome_size)
  [ Chromosome(create_genes(chromosome_size)) for _ in 1:size ]
end

score(population) = map(x -> sum(x.genes), population)

function fight(scores, index1, index2)
  scores[index1] > scores[index2] ? index1 : index2
end

function tournament(scores)
  population_size = length(scores)
  selection_size = population_size * 2
  [ fight(scores, rand(1:population_size), rand(1:population_size))
    for _ in 1:selection_size ]
end

function crossover(chromosome1, chromosome2)
  cut_point = rand(1:length(chromosome1.genes))
  first_part = chromosome1.genes[1:cut_point]
  second_part = chromosome2.genes[cut_point + 1:end]
  mutate(Chromosome([ first_part, second_part]))
end

function reproduction(new_population, current_population, selection)
  if selection == []
    return new_population
  else
    father = current_population[selection[1]]
    mother = current_population[selection[2]]
    child = crossover(father, mother)
    reproduction([new_population, child], current_population, selection[3:end])
  end
end

function mutate(ch)
  mutator(g) = if rand(1:10) == 1
    g == 1 ? 0 : 1
  else
    g
  end
  Chromosome([ mutator(x) for x in ch.genes ])
end

J’ai simplement ajouter la fonction mutate dans crossover:

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function crossover(chromosome1, chromosome2)
  cut_point = rand(1:length(chromosome1.genes))
  first_part = chromosome1.genes[1:cut_point]
  second_part = chromosome2.genes[cut_point + 1:end]
  mutate(Chromosome([ first_part, second_part]))
end

Et ça fonctionne:

julia> include("main.jl")

julia> pop = create_population(8, 20)
8-element Array{Chromosome,1}:
 Chromosome([0,0,0,1,1,1,0,0,0,0,0,1,1,0,1,1,0,1,0,1])
 Chromosome([1,0,1,1,0,0,0,0,1,1,0,1,1,0,1,0,1,0,1,1])
 Chromosome([1,0,1,1,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1])
 Chromosome([1,1,1,1,1,0,0,0,0,0,1,1,0,1,1,0,1,0,0,1])
 Chromosome([0,1,1,0,0,0,0,0,0,1,1,1,0,1,0,0,1,0,1,0])
 Chromosome([0,1,0,1,1,0,0,1,0,0,1,1,1,1,0,0,0,0,0,1])
 Chromosome([1,1,1,0,0,0,1,1,0,1,1,0,0,0,1,1,1,0,0,0])
 Chromosome([1,0,0,0,1,0,0,1,0,1,0,1,0,0,0,0,1,0,0,0])

julia> scores = score(pop)
8-element Array{Int32,1}:
  9
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  9
 11
  8
  9
 10
  6

julia> selection = tournament(scores)
16-element Array{Int32,1}:
 7
 7
 7
 7
 ...
 4
 1
 1

julia> gen2 = reproduction([], pop, selection)
8-element Array{Chromosome,1}:
 Chromosome({1,1,1,0,0,0,1,1,0,1,1,0,1,0,1,1,1,0,0,0})
 Chromosome({1,1,1,0,0,0,1,0,0,1,1,0,0,0,1,1,0,0,0,0})
 Chromosome({0,1,0,0,1,0,0,1,0,1,1,1,1,0,1,0,1,0,1,1})
 Chromosome({1,0,1,0,0,1,0,0,1,1,1,1,0,1,0,0,1,0,1,1})
 Chromosome({1,0,1,1,0,0,0,0,1,0,0,1,1,0,1,1,0,0,0,1})
 Chromosome({1,1,1,1,1,1,0,0,0,0,1,1,0,1,1,1,1,0,0,0})
 Chromosome({0,1,1,0,0,0,1,0,0,0,1,1,0,1,1,0,1,0,0,1})
 Chromosome({0,0,0,1,1,1,0,0,0,1,0,1,1,0,1,1,0,1,0,1})

Mais un truc me dérange toujours, le tableau est modifié après passage dans mutate:

julia> c = chromosome(create_genes(10))
chromosome([0,0,0,1,1,1,1,1,0,1])

julia> d = mutate(c)
Chromosome({0,0,0,1,1,1,1,1,0,1})

julia> c.genes
10-element Array{Int32,1}:
...

julia> d.genes
10-element Array{Any,1}:
...

C’est bien ça, le tableau n’est pas du même type. Bon, ça ne dérange pas l’algorithme et c’est l’occasion d’un prochain article sur Julia ;)

À demain.

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