Update Julia to match version

.gitignore

@@ -0,0 +1,25 @@
+# Files generated by invoking Julia with --code-coverage
+*.jl.cov
+*.jl.*.cov
+
+# Files generated by invoking Julia with --track-allocation
+*.jl.mem
+
+# System-specific files and directories generated by the BinaryProvider and BinDeps packages
+# They contain absolute paths specific to the host computer, and so should not be committed
+deps/deps.jl
+deps/build.log
+deps/downloads/
+deps/usr/
+deps/src/
+
+# Build artifacts for creating documentation generated by the Documenter package
+docs/build/
+docs/site/
+
+# File generated by Pkg, the package manager, based on a corresponding Project.toml
+# It records a fixed state of all packages used by the project. As such, it should not be
+# committed for packages, but should be committed for applications that require a static
+# environment.
+Manifest.toml
+./idea

Project.toml

@@ -0,0 +1,8 @@
+name = "GeneticAlgorithms"
+uuid = "d063b88d-234b-4720-a79b-f4478e1df7b8"
+authors = ["ceci.merelo <[email protected]>"]
+version = "0.1.0"
+
+[deps]
+Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"

README.md

@@ -21,7 +21,7 @@ end
 Your entity should inherit from the abstract `GeneticAlgorithms.Entity`. The framework will look for a `create_entity` function and will use it to create an initial population.
 
 ```julia
-type EqualityMonster <: Entity
+struct EqualityMonster <: Entity
     abcde::Array
     fitness
 

src/GeneticAlgorithms.jl

@@ -3,7 +3,7 @@ module GeneticAlgorithms
 
 # -------
 
-importall Base
+using Base, Random, Distributed
 
 export  Entity,
         GAmodel,
@@ -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
 
@@ -34,15 +34,15 @@ end
 
 # -------
 
-type GAmodel
+mutable struct GAmodel
     initial_pop_size::Int
     gen_num::Int
 
     population::Array
     pop_data::Array{EntityData}
     freezer::Array{EntityData}
 
-    rng::AbstractRNG
+    rng::MersenneTwister
 
     ga
 
@@ -96,18 +96,18 @@ function runga(model::GAmodel)
     while true
         evaluate_population(model)
 
-        grouper = @task model.ga.group_entities(model.population)
-        groupings = Any[]
-        while !istaskdone(grouper)
-            group = consume(grouper)
-            group != nothing && push!(groupings, group)
-        end
+        chnl = Channel{Int}(1)
+        @async for i in 2:length(pop)
+            put!(c, (pop[1], i))
+        end;
+        close(chnl)
 
-        if length(groupings) < 1
+        group = [ x for x in chnl ]
+        if length(group) < 1
             break
         end
 
-        crossover_population(model, groupings)
+        crossover_population(model, group)
         mutate_population(model)
     end
 

test/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
 
@@ -19,7 +19,7 @@ end
 
 function create_entity(num)
     # for simplicity sake, let's limit the values for abcde to be integers in [-42, 42]
-    EqualityMonster(rand(Int, 5) % 43)
+    EqualityMonster(rand(-43:43, 5))
 end
 
 function fitness(ent)
@@ -31,19 +31,6 @@ function fitness(ent)
     abs(score - 42)
 end
 
-function group_entities(pop)
-    println("BEST: ", pop[1])
-
-    if pop[1].fitness == 0
-        return
-    end
-
-    # simple naive groupings that pair the best entitiy with every other
-    for i in 1:length(pop)
-        produce([1, i])
-    end
-end
-
 function crossover(group)
     child = EqualityMonster()
 

test/test_run.jl

@@ -0,0 +1,6 @@
+using GeneticAlgorithms
+include("equalityga.jl")
+
+model = runga(equalityga; initial_pop_size = 16)
+
+population(model)

test/testga.jl

@@ -1,11 +1,11 @@
 
-using GeneticAlgorithms
+using GeneticAlgorithms, Distributed
 
 module testga
 
     using GeneticAlgorithms
 
-    type TestMonster <: Entity
+    mutable struct TestMonster <: Entity
         genes
         fitness
 
@@ -23,6 +23,10 @@ module testga
         entity.genes
     end
 
+    function isless(lhs::TestMonster, rhs::TestMonster)
+        abs(lhs.fitness) > abs(rhs.fitness)
+    end
+
     function group_entities(population)
         if population[1].fitness >= 16
             return