diff --git a/src/algorithm/NSGAII.jl b/src/algorithm/NSGAII.jl
new file mode 100644
index 0000000..8eaa718
--- /dev/null
+++ b/src/algorithm/NSGAII.jl
@@ -0,0 +1,61 @@
+using Dates
+
+mutable struct NSGAII <: Algorithm
+  problem::Problem
+  populationSize::Int
+  numberOfEvaluations::Int
+
+  foundSolutions::Vector
+
+  termination::Termination
+  mutation::MutationOperator
+  crossover::CrossoverOperator
+
+  solver::EvolutionaryAlgorithm
+
+  dominanceComparator::Function
+
+  function NSGAII() 
+    algorithm = new()
+    algorithm.solver = EvolutionaryAlgorithm()
+    algorithm.solver.name = "NSGA-II"
+
+    algorithm.dominanceComparator = compareForDominance
+    return algorithm
+  end
+end
+
+function nsgaII(nsgaII::NSGAII) 
+  solver = nsgaII.solver 
+  
+  solver.problem = nsgaII.problem
+  solver.populationSize = nsgaII.populationSize
+  solver.offspringPopulationSize = nsgaII.populationSize
+
+  solver.solutionsCreation = DefaultSolutionsCreation((problem = solver.problem, numberOfSolutionsToCreate = solver.populationSize))
+
+  solver.evaluation = SequentialEvaluation((problem = solver.problem, ))
+
+  solver.termination = nsgaII.termination
+  solver.variation = CrossoverAndMutationVariation(solver.offspringPopulationSize, nsgaII.crossover, nsgaII.mutation)
+
+  solver.replacement = RankingAndDensityEstimatorReplacement(nsgaII.dominanceComparator)
+
+  solver.selection = BinaryTournamentSelection(solver.variation.matingPoolSize, compareRankingAndCrowdingDistance)
+
+  return evolutionaryAlgorithm(solver)
+end
+
+function optimize(algorithm::NSGAII)
+  algorithm.foundSolutions = nsgaII(algorithm)
+  
+  return Nothing
+end
+
+function name(nsgaII::NSGAII)
+  return name(nsgaII.solver)
+end
+
+function getObservable(nsgaII::NSGAII)
+  return getObservable(nsgaII.solver)
+end
\ No newline at end of file
diff --git a/src/algorithm/algorithm.jl b/src/algorithm/algorithm.jl
deleted file mode 100644
index 355e2e4..0000000
--- a/src/algorithm/algorithm.jl
+++ /dev/null
@@ -1,136 +0,0 @@
-using Dates
-
-mutable struct EvolutionaryAlgorithm <: Algorithm
-  name::String
-  problem::Problem
-  populationSize::Int
-  offspringPopulationSize::Int
-
-  foundSolutions::Vector
-
-  solutionsCreation::SolutionsCreation
-  evaluation::Evaluation
-  termination::Termination
-  selection::Selection
-  variation::Variation
-  replacement::Replacement
-
-  observable::Observable
-
-  status::Dict
-
-  function EvolutionaryAlgorithm() 
-    x = new()
-    x.name = "EA"
-    x.observable = Observable("EA observable")
-    return return x
-  end
-end
-
-function getObservable(algorithm::EvolutionaryAlgorithm)
-  return algorithm.observable
-end
-
-function evolutionaryAlgorithm(ea::EvolutionaryAlgorithm)
-  startingTime = Dates.now()
-
-  population = ea.solutionsCreation.create(ea.solutionsCreation.parameters)
-  population = ea.evaluation.evaluate(population, ea.evaluation.parameters)
-
-  evaluations = length(population)
-  ea.status = Dict("EVALUATIONS" => evaluations, "POPULATION" => population, "COMPUTING_TIME" => (Dates.now() - startingTime))
-
-  notify(ea.observable, ea.status)
-
-  while !ea.termination.isMet(ea.status, ea.termination.parameters)
-    matingPool = ea.selection.select(population, ea.selection.parameters)
-    
-    offspringPopulation = ea.variation.variate(population, matingPool, ea.variation.parameters)
-    offspringPopulation = ea.evaluation.evaluate(offspringPopulation, ea.evaluation.parameters)
-
-    population = ea.replacement.replace(population, offspringPopulation, ea.replacement.parameters)
-
-    evaluations += length(offspringPopulation)
-    ea.status["EVALUATIONS"] = evaluations
-    ea.status["POPULATION"] = population
-    ea.status["COMPUTING_TIME"] = Dates.now() - startingTime
-
-    notify(ea.observable, ea.status)
-  end
-
-  foundSolutions = population
-  return foundSolutions
-end
-
-function optimize(algorithm::EvolutionaryAlgorithm)
-  algorithm.foundSolutions = evolutionaryAlgorithm(algorithm)
-  
-  return Nothing
-end
-
-function name(algorithm::EvolutionaryAlgorithm)
-  return algorithm.name
-end
-
-
-#################################
-
-mutable struct NSGAII <: Algorithm
-  problem::Problem
-  populationSize::Int
-  numberOfEvaluations::Int
-
-  foundSolutions::Vector
-
-  termination::Termination
-  mutation::MutationOperator
-  crossover::CrossoverOperator
-
-  solver::EvolutionaryAlgorithm
-
-  dominanceComparator::Function
-
-  function NSGAII() 
-    algorithm = new()
-    algorithm.solver = EvolutionaryAlgorithm()
-    algorithm.solver.name = "NSGA-II"
-
-    algorithm.dominanceComparator = compareForDominance
-    return algorithm
-  end
-end
-
-function nsgaII(nsgaII::NSGAII) 
-  solver = nsgaII.solver 
-  
-  solver.problem = nsgaII.problem
-  solver.populationSize = nsgaII.populationSize
-  solver.offspringPopulationSize = nsgaII.populationSize
-
-  solver.solutionsCreation = DefaultSolutionsCreation((problem = solver.problem, numberOfSolutionsToCreate = solver.populationSize))
-
-  solver.evaluation = SequentialEvaluation((problem = solver.problem, ))
-
-  solver.termination = nsgaII.termination
-  solver.variation = CrossoverAndMutationVariation((offspringPopulationSize = solver.offspringPopulationSize, crossover = nsgaII.crossover, mutation = nsgaII.mutation))
-
-  solver.replacement = RankingAndDensityEstimatorReplacement((dominanceComparator = nsgaII.dominanceComparator, ))
-
-  solver.selection = BinaryTournamentSelection((matingPoolSize = solver.variation.matingPoolSize, comparator = compareRankingAndCrowdingDistance))
-
-  return evolutionaryAlgorithm(solver)
-end
-
-function optimize(algorithm::NSGAII)
-  algorithm.foundSolutions = nsgaII(algorithm)
-  
-  return Nothing
-end
-
-function name(nsgaII::NSGAII)
-  return name(nsgaII.solver)
-end
-
-function getObservable(nsgaII::NSGAII)
-  return getObservable(nsgaII.solver)
-end
\ No newline at end of file
diff --git a/src/algorithm/evolutionaryAlgorithm.jl b/src/algorithm/evolutionaryAlgorithm.jl
new file mode 100644
index 0000000..7c4f8cf
--- /dev/null
+++ b/src/algorithm/evolutionaryAlgorithm.jl
@@ -0,0 +1,74 @@
+using Dates
+
+mutable struct EvolutionaryAlgorithm <: Algorithm
+  name::String
+  problem::Problem
+  populationSize::Int
+  offspringPopulationSize::Int
+
+  foundSolutions::Vector
+
+  solutionsCreation::SolutionsCreation
+  evaluation::Evaluation
+  termination::Termination
+  selection::Selection
+  variation::Variation
+  replacement::Replacement
+
+  observable::Observable
+
+  status::Dict
+
+  function EvolutionaryAlgorithm() 
+    x = new()
+    x.name = "Evolutionary algorithm"
+    x.observable = Observable("EvolutionaryAlgorithm observable")
+    return return x
+  end
+end
+
+function getObservable(algorithm::EvolutionaryAlgorithm)
+  return algorithm.observable
+end
+
+function evolutionaryAlgorithm(ea::EvolutionaryAlgorithm)
+  startingTime = Dates.now()
+
+  population = create(ea.solutionsCreation)
+  population = evaluate(population, ea.evaluation)
+
+  evaluations = length(population)
+  ea.status = Dict("EVALUATIONS" => evaluations, "POPULATION" => population, "COMPUTING_TIME" => (Dates.now() - startingTime))
+
+  notify(ea.observable, ea.status)
+
+  while !isMet(ea.status, ea.termination)
+    matingPool = select(population, ea.selection)
+    
+    offspringPopulation = variate(population, matingPool, ea.variation)
+    offspringPopulation = evaluate(offspringPopulation, ea.evaluation)
+
+    population = replace_(population, offspringPopulation, ea.replacement)
+
+    evaluations += length(offspringPopulation)
+    ea.status["EVALUATIONS"] = evaluations
+    ea.status["POPULATION"] = population
+    ea.status["COMPUTING_TIME"] = Dates.now() - startingTime
+
+    notify(ea.observable, ea.status)
+  end
+
+  foundSolutions = population
+  return foundSolutions
+end
+
+function optimize(algorithm::EvolutionaryAlgorithm)
+  algorithm.foundSolutions = evolutionaryAlgorithm(algorithm)
+  
+  return Nothing
+end
+
+function name(algorithm::EvolutionaryAlgorithm)
+  return algorithm.name
+end
+
diff --git a/src/algorithm/localSearch.jl b/src/algorithm/localSearch.jl
index 21ceb69..eec00e0 100644
--- a/src/algorithm/localSearch.jl
+++ b/src/algorithm/localSearch.jl
@@ -4,6 +4,10 @@ mutable struct LocalSearch <: Algorithm
   numberOfIterations::Int
   mutation::MutationOperator
   foundSolution::Solution
+
+  function LocalSearch(startingSolution, problem, numberOfIterations, mutation)
+    return new(startingSolution, problem, numberOfIterations, mutation, copySolution(startingSolution))
+  end
 end
 
 function optimize(algorithm::LocalSearch)::Solution
@@ -14,7 +18,7 @@ function optimize(algorithm::LocalSearch)::Solution
 
   for _ in 1:numberOfIterations
     mutatedSolution = copySolution(algorithm.startingSolution)
-    mutatedSolution.variables = mutate(mutatedSolution.variables, mutation)
+    mutatedSolution = mutate(mutatedSolution, mutation)
     
     mutatedSolution = evaluate(mutatedSolution, problem)
 
@@ -23,7 +27,7 @@ function optimize(algorithm::LocalSearch)::Solution
     end
   end
 
-  algorithm.foundSolution = foundSolution
+  algorithm.foundSolution = currentSolution
 
   return currentSolution
 end
diff --git a/src/metajul.jl b/src/metajul.jl
index 2582df6..a766560 100644
--- a/src/metajul.jl
+++ b/src/metajul.jl
@@ -69,6 +69,9 @@ include("component/evolutionaryAlgorithm/replacement.jl")
 export normalizeObjectives, distanceBasedSubsetSelection
 include("util/utils.jl")
 
+export Observable, EvaluationObserver
+include("util/observer.jl")
+
 export BinaryProblem, ContinuousProblem, constrainedProblem
 export addObjective, addVariable, addConstraint, createSolution, evaluate, setName, name
 export bounds
@@ -88,5 +91,8 @@ include("problem/multiObjective/kursawe.jl")
 include("problem/multiObjective/ZDT.jl")
 include("problem/multiObjective/oneZeroMax.jl")
 
+export LocalSearch
+export optimize
+include("algorithm/localSearch.jl")
 
 end # module metajul
diff --git a/test/algorithm/localSearchTest.jl b/test/algorithm/localSearchTest.jl
new file mode 100644
index 0000000..ef3e105
--- /dev/null
+++ b/test/algorithm/localSearchTest.jl
@@ -0,0 +1,29 @@
+function localSearchIsProperlyInitialized()
+    problem = schaffer()
+    startingSolution = createSolution(problem)
+    numberOfIterations = 10
+    mutation = PolynomialMutation(1.0 / numberOfVariables(problem), 20.0, problem.bounds)
+
+    algorithm = LocalSearch(startingSolution, problem, numberOfIterations, mutation)
+
+    return problem == algorithm.problem && numberOfIterations == algorithm.numberOfIterations && mutation == algorithm.mutation && startingSolution == algorithm.startingSolution
+end
+
+function localSearchWithZeroIterationsReturnsTheStartingSolution()
+    problem = schaffer()
+    startingSolution = createSolution(problem)
+    numberOfIterations = 0
+    mutation = UniformMutation(0.01, 20.0, problem.bounds)
+
+    algorithm = LocalSearch(startingSolution, problem, numberOfIterations, mutation)
+    optimize(algorithm)
+
+    return startingSolution == algorithm.foundSolution
+end
+
+
+@testset "Constraint handling functions tests" begin
+    @test localSearchIsProperlyInitialized()
+    @test localSearchWithZeroIterationsReturnsTheStartingSolution()
+end
+
diff --git a/test/runtests.jl b/test/runtests.jl
index d15d2b1..3bde7dd 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -78,3 +78,11 @@ problemTests = [
 for testProgram in problemTests
     include(testProgram)
 end
+
+algorithmTests = [
+    "algorithm/localSearchTest.jl",
+]
+
+for testProgram in algorithmTests
+    include(testProgram)
+end