Merge pull request #11 from Nikola-Mircic/genetic_algorithm

Genetic algorithm

src/FTN_PSI_PA.jl

@@ -2,6 +2,8 @@ module FTN_PSI_PA
 
 import Printf
 
+export GeneticAlgorithm
+
 include("genetic_algorithm/genetic_algorithm.jl")
 include("pso/pso.jl")
 

src/genetic_algorithm/genetic_algorithm.jl

@@ -2,6 +2,20 @@ module GeneticAlgorithm
 
 include("crossover.jl")
 
+export makeCrossoverFunc
+export generatePopulation
+
+export fitFunction
+
+export geneticAlgorithm
+
+export getAverage
+export analyzeElitePercentage
+export analyzeMutationPercentage
+export analyzeNumberOfIteration
+export analyzePopulationSize
+
+export printResult
 
 function geneticAlgorithm(population::Vector{Entity}, elitePercent::Float64, mutationPercent::Float64, crossoverFunc!::Function, iter::Int)
     bestFitnes = []
@@ -58,7 +72,7 @@ function getAverage(population::Vector{Entity}, elitePercent::Float64, mutationP
                                        elitePercent, 
                                        mutationPercent, 
                                        crossoverFunc!, 
-                                       numOfIterations)
+                                       iter)
         avg_gen += gen_i
         avg_best += best_i.fitness
     end
@@ -150,13 +164,4 @@ function printResult(values, results)
     end
 end
 
-export geneticAlgorithm
-export getAverage
-export analyzeElitePercentage
-export analyzeMutationPercentage
-export analyzeNumberOfIteration
-export analyzePopulationSize
-
-export printResult
-
 end

test/genetic_algorithm.jl

@@ -0,0 +1,29 @@
+using FTN_PSI_PA.GeneticAlgorithm
+
+@testset "GeneticAlgorithm" begin
+    #=
+    |   Find four numbers that add up to 143.
+    |
+    |   x1 + x2 + x3 + x4 = 143
+    |   x1=?, x2=?, x3=?, x4=?
+    =#
+
+    populationSize = 100
+    genesLength = 4
+    minGene = 0
+    maxGene = 143
+
+    elitePercent = 0.15
+    mutationPercent = 0.2
+    numOfIterations = 100
+
+    population = generatePopulation(populationSize, genesLength, minGene, maxGene)
+
+    num_gen, best = getAverage(population, 
+                                    elitePercent, 
+                                    mutationPercent, 
+                                    makeCrossoverFunc([1;3]), 
+                                    numOfIterations)
+
+    @test best < 0.1 # Test if the absolute error is smaller than 0.1
+end

test/runtests.jl

@@ -1,6 +1,6 @@
+using FTN_PSI_PA
 using Test
 
 @testset "Tests" begin
-    a = 5
-    @test a == 5 
+    include("genetic_algorithm.jl")
 end