Migration v0.6

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
 

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
 
@@ -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

test/testga.jl

@@ -5,7 +5,7 @@ module testga
 
     using GeneticAlgorithms
 
-    type TestMonster <: Entity
+    mutable struct TestMonster <: Entity
         genes
         fitness