diff --git a/test/yulPhaser/Population.cpp b/test/yulPhaser/Population.cpp
index dfee5f74a..98532c9dc 100644
--- a/test/yulPhaser/Population.cpp
+++ b/test/yulPhaser/Population.cpp
@@ -182,38 +182,6 @@ BOOST_FIXTURE_TEST_CASE(makeRandom_should_compute_fitness, PopulationFixture)
 	BOOST_TEST(population.individuals()[2].fitness == m_fitnessMetric->evaluate(population.individuals()[2].chromosome));
 }
 
-BOOST_FIXTURE_TEST_CASE(run_should_not_make_fitness_of_top_chromosomes_worse, PopulationFixture)
-{
-	stringstream output;
-	vector<Chromosome> chromosomes = {
-		Chromosome(vector<string>{StructuralSimplifier::name}),
-		Chromosome(vector<string>{BlockFlattener::name}),
-		Chromosome(vector<string>{SSAReverser::name}),
-		Chromosome(vector<string>{UnusedPruner::name}),
-		Chromosome(vector<string>{StructuralSimplifier::name, BlockFlattener::name}),
-	};
-	Population population(m_fitnessMetric, chromosomes);
-
-	size_t initialTopFitness[2] = {
-		m_fitnessMetric->evaluate(chromosomes[0]),
-		m_fitnessMetric->evaluate(chromosomes[1]),
-	};
-
-	for (int i = 0; i < 6; ++i)
-	{
-		population.run(1, output);
-		BOOST_TEST(population.individuals().size() == 5);
-
-		size_t currentTopFitness[2] = {
-			population.individuals()[0].fitness,
-			population.individuals()[1].fitness,
-		};
-		BOOST_TEST(currentTopFitness[0] <= initialTopFitness[0]);
-		BOOST_TEST(currentTopFitness[1] <= initialTopFitness[1]);
-		BOOST_TEST(currentTopFitness[0] <= currentTopFitness[1]);
-	}
-}
-
 BOOST_FIXTURE_TEST_CASE(plus_operator_should_add_two_populations, PopulationFixture)
 {
 	BOOST_CHECK_EQUAL(
diff --git a/tools/yulPhaser/Population.cpp b/tools/yulPhaser/Population.cpp
index 12acbe417..b39f5dad5 100644
--- a/tools/yulPhaser/Population.cpp
+++ b/tools/yulPhaser/Population.cpp
@@ -84,18 +84,6 @@ Population Population::makeRandom(
 	);
 }
 
-void Population::run(optional<size_t> _numRounds, ostream& _outputStream)
-{
-	for (size_t round = 0; !_numRounds.has_value() || round < _numRounds.value(); ++round)
-	{
-		doMutation();
-		doSelection();
-
-		_outputStream << "---------- ROUND " << round << " ----------" << endl;
-		_outputStream << *this;
-	}
-}
-
 Population Population::select(Selection const& _selection) const
 {
 	vector<Individual> selectedIndividuals;
@@ -131,35 +119,6 @@ ostream& phaser::operator<<(ostream& _stream, Population const& _population)
 	return _stream;
 }
 
-void Population::doMutation()
-{
-	// TODO: Implement mutation and crossover
-}
-
-void Population::doSelection()
-{
-	randomizeWorstChromosomes(*m_fitnessMetric, m_individuals, m_individuals.size() / 2);
-	m_individuals = sortedIndividuals(move(m_individuals));
-}
-
-void Population::randomizeWorstChromosomes(
-	FitnessMetric const& _fitnessMetric,
-	vector<Individual>& _individuals,
-	size_t _count
-)
-{
-	assert(_individuals.size() >= _count);
-	// ASSUMPTION: _individuals is sorted in ascending order
-
-	auto individual = _individuals.begin() + (_individuals.size() - _count);
-	for (; individual != _individuals.end(); ++individual)
-	{
-		auto chromosome = Chromosome::makeRandom(binomialChromosomeLength(MaxChromosomeLength));
-		size_t fitness = _fitnessMetric.evaluate(chromosome);
-		*individual = {move(chromosome), fitness};
-	}
-}
-
 vector<Individual> Population::chromosomesToIndividuals(
 	FitnessMetric const& _fitnessMetric,
 	vector<Chromosome> _chromosomes
diff --git a/tools/yulPhaser/Population.h b/tools/yulPhaser/Population.h
index f66efc9a0..f4f42346d 100644
--- a/tools/yulPhaser/Population.h
+++ b/tools/yulPhaser/Population.h
@@ -69,19 +69,19 @@ struct Individual
 bool isFitter(Individual const& a, Individual const& b);
 
 /**
- * Represents a changing set of individuals undergoing a genetic algorithm.
- * Each round of the algorithm involves mutating existing individuals, evaluating their fitness
- * and selecting the best ones for the next round.
+ * Represents a snapshot of a population undergoing a genetic algorithm. Consists of a set of
+ * chromosomes with associated fitness values.
  *
  * An individual is a sequence of optimiser steps represented by a @a Chromosome instance.
  * Individuals are always ordered by their fitness (based on @_fitnessMetric and @a isFitter()).
  * The fitness is computed using the metric as soon as an individual is inserted into the population.
+ *
+ * The population is immutable. Selections, mutations and crossover work by producing a new
+ * instance and copying the individuals.
  */
 class Population
 {
 public:
-	static constexpr size_t MaxChromosomeLength = 30;
-
 	explicit Population(
 		std::shared_ptr<FitnessMetric const> _fitnessMetric,
 		std::vector<Chromosome> _chromosomes = {}
@@ -103,7 +103,6 @@ public:
 		size_t _maxChromosomeLength
 	);
 
-	void run(std::optional<size_t> _numRounds, std::ostream& _outputStream);
 	Population select(Selection const& _selection) const;
 	friend Population (::operator+)(Population _a, Population _b);
 
@@ -123,14 +122,6 @@ private:
 		m_fitnessMetric(std::move(_fitnessMetric)),
 		m_individuals{sortedIndividuals(std::move(_individuals))} {}
 
-	void doMutation();
-	void doSelection();
-
-	static void randomizeWorstChromosomes(
-		FitnessMetric const& _fitnessMetric,
-		std::vector<Individual>& _individuals,
-		size_t _count
-	);
 	static std::vector<Individual> chromosomesToIndividuals(
 		FitnessMetric const& _fitnessMetric,
 		std::vector<Chromosome> _chromosomes