Develop

.travis.yml

@@ -1,16 +1,35 @@
-language: cpp
-compiler:
-    - gcc
+## Documentation: http://docs.travis-ci.com/user/languages/julia/
+language: julia
+os:
+  - linux
+  - osx
+julia:
+  - 0.6
+  - nightly
 notifications:
-    email: false
-before_install:
-    - sudo add-apt-repository ppa:staticfloat/julia-deps -y
-    - sudo add-apt-repository ppa:staticfloat/julianightlies -y
-    - sudo apt-get update -qq -y
-    - sudo apt-get install git libpcre3-dev julia -y
-    - git config --global user.name "Travis User"
-    - git config --global user.email "[email protected]"
-    - if [[ -a .git/shallow ]]; then git fetch --unshallow; fi
-script:
-    - julia -e 'versioninfo(); Pkg.init(); Pkg.clone(pwd())'
-    - julia ./test/runtests.jl
+  email: false
+git:
+  depth: 99999999
+
+## uncomment the following lines to allow failures on nightly julia
+## (tests will run but not make your overall status red)
+#matrix:
+#  allow_failures:
+#  - julia: nightly
+
+## uncomment and modify the following lines to manually install system packages
+#addons:
+#  apt: # apt-get for linux
+#    packages:
+#    - gfortran
+#before_script: # homebrew for mac
+#  - if [ $TRAVIS_OS_NAME = osx ]; then brew install gcc; fi
+
+## uncomment the following lines to override the default test script
+#script:
+#  - julia -e 'Pkg.clone(pwd()); Pkg.build("Bedgraph"); Pkg.test("Bedgraph"; coverage=true)'
+after_success:
+  # push coverage results to Coveralls
+  # - julia -e 'cd(Pkg.dir("GeneticAlgorithms")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())'
+  # push coverage results to Codecov
+  # - julia -e 'cd(Pkg.dir("GeneticAlgorithms")); Pkg.add("Coverage"); using Coverage; Codecov.submit(Codecov.process_folder())'

README.md

@@ -113,11 +113,9 @@ end
 
 ```julia
 using GeneticAlgorithms
-require("GeneticAlgorithms/test/equalityga.jl")
+include(joinpath(@__DIR__, "equalityga.jl"))
 
 model = runga(equalityga; initial_pop_size = 16)
 
 population(model)  # the the latest population when the GA exited
 ```
-
-

test/equalityga.jl → examples/equalityga.jl

@@ -5,11 +5,11 @@ import Base.isless
 
 using GeneticAlgorithms
 
-type EqualityMonster <: Entity
+mutable struct EqualityMonster <: Entity
     abcde::Array
     fitness
 
-    EqualityMonster() = new(Array(Int, 5), nothing)
+    EqualityMonster() = new(Array{Int}(5), nothing)
     EqualityMonster(abcde) = new(abcde, nothing)
 end
 
@@ -31,7 +31,7 @@ function fitness(ent)
     abs(score - 42)
 end
 
-function group_entities(pop)
+function group_entities(grouped::Channel, pop)
     println("BEST: ", pop[1])
 
     if pop[1].fitness == 0
@@ -40,7 +40,7 @@ function group_entities(pop)
 
     # simple naive groupings that pair the best entitiy with every other
     for i in 1:length(pop)
-        produce([1, i])
+        put!(grouped, [1, i])
     end
 end
 
@@ -50,7 +50,7 @@ function crossover(group)
     # grab each element from a random parent
     num_parents = length(group)
     for i in 1:length(group[1].abcde)
-        parent = (rand(Uint) % num_parents) + 1
+        parent = (rand(UInt) % num_parents) + 1
         child.abcde[i] = group[parent].abcde[i]
     end
 
@@ -61,8 +61,8 @@ function mutate(ent)
     # let's go crazy and mutate 20% of the time
     rand(Float64) < 0.8 && return
 
-    rand_element = rand(Uint) % 5 + 1
+    rand_element = rand(UInt) % 5 + 1
     ent.abcde[rand_element] = rand(Int) % 43
 end
 
-end
+end

examples/example-parallel.jl

@@ -0,0 +1,10 @@
+using GeneticAlgorithms
+addprocs(2)
+println("nprocs: $(nprocs())")
+
+push!(LOAD_PATH, @__DIR__)
+@everywhere using equalityga
+
+model = runga(equalityga; initial_pop_size = 16)
+
+population(model) # the the latest population when the GA exited

examples/example.jl

@@ -0,0 +1,6 @@
+using GeneticAlgorithms
+include(joinpath(@__DIR__, "equalityga.jl"))
+
+model = runga(equalityga; initial_pop_size = 16)
+
+population(model) # the the latest population when the GA exited

src/GeneticAlgorithms.jl

@@ -16,15 +16,15 @@ export  Entity,
 
 # -------
 
-abstract Entity
+abstract type Entity end
 
 isless(lhs::Entity, rhs::Entity) = lhs.fitness < rhs.fitness
 
 fitness!(ent::Entity, fitness_score) = ent.fitness = fitness_score
 
 # -------
 
-type EntityData
+struct EntityData
     entity
     generation::Int
 
@@ -34,7 +34,7 @@ end
 
 # -------
 
-type GAmodel
+mutable struct GAmodel
     initial_pop_size::Int
     gen_num::Int
 
@@ -96,11 +96,18 @@ function runga(model::GAmodel)
     while true
         evaluate_population(model)
 
-        grouper = @task model.ga.group_entities(model.population)
+        # Setup channel for inter-task communication.
+        grouped = Channel(0);
+        grouper = @schedule model.ga.group_entities(grouped, model.population)
+
+        # Associates the lifetime of grouped channel with the grouping task.
+        bind(grouped, grouper); # This will close the channel once the grouper task has finished.
+
         groupings = Any[]
-        while !istaskdone(grouper)
-            group = consume(grouper)
-            group != nothing && push!(groupings, group)
+
+        # Loop runs as long as the Channel has data or is open. The loop is terminated once the Channel is closed and emptied.
+        for group in grouped
+            push!(groupings, group)
         end
 
         if length(groupings) < 1

test/testga.jl

@@ -5,7 +5,7 @@ module testga
 
     using GeneticAlgorithms
 
-    type TestMonster <: Entity
+    mutable struct TestMonster <: Entity
         genes
         fitness
 
@@ -23,15 +23,15 @@ module testga
         entity.genes
     end
 
-    function group_entities(population)
+    function group_entities(grouped::Channel, population)
         if population[1].fitness >= 16
             return
         end
 
         freeze(population[1])
 
         for i in 1:length(population)
-            produce([1, i])
+            put!(grouped, [1, i])
         end
     end