diff --git a/examples/gno.land/r/demo/games/ga/ga.gno b/examples/gno.land/r/demo/games/ga/ga.gno
new file mode 100644
index 00000000000..ed2476827bc
--- /dev/null
+++ b/examples/gno.land/r/demo/games/ga/ga.gno
@@ -0,0 +1,168 @@
+package ga
+
+import (
+	"math"
+
+	"gno.land/p/demo/entropy"
+)
+
+// Individual represents a single solution in the population
+type Individual struct {
+	Genome  string
+	Fitness float64
+}
+
+// Target is the string we want to evolve
+const Target = "HEY"
+
+// MutationRate controls how often mutations occur
+const MutationRate = 0.01
+
+// PopulationSize defines the number of individuals in the population
+const PopulationSize = 100
+
+// Charset defines the possible characters in the genome
+var Charset = []rune("ABCDEFGHIJKLMNOPQRSTUVWXYZ, !")
+
+func Start(ei *entropy.Instance) (int, string) {
+	var generation = 0
+	// Step 1: Initialize Population
+	population := initializePopulation(PopulationSize, ei)
+
+	for generation < 200 {
+		// Step 2: Calculate Fitness
+		for i := range population {
+			population[i].Fitness = calculateFitness(population[i].Genome)
+		}
+
+		// Step 3: Check for Solution
+		best := findBestIndividual(population)
+		//fmt.Printf("Generation %d: %s (Fitness: %f)\n", generation, best.Genome, best.Fitness)
+		if best.Genome == Target {
+			println("Target reached!")
+			return generation, string(best.Genome)
+		}
+
+		// Step 4: Generate Next Generation
+		population = generateNextGeneration(population, ei)
+
+		generation++
+	}
+	return generation, ""
+}
+
+// initializePopulation creates a random population of individuals
+func initializePopulation(size int, ei *entropy.Instance) []Individual {
+	population := make([]Individual, size)
+	for i := 0; i < size; i++ {
+		population[i] = Individual{
+			Genome: randomGenome(len(Target), ei), // new random each loop
+		}
+	}
+	return population
+}
+
+// randomGenome generates a random string of the same length as the target
+func randomGenome(length int, ei *entropy.Instance) string {
+	genome := make([]rune, length)
+	for i := 0; i < length; i++ {
+		index := ei.Value() % uint32(len(Charset))
+		genome[i] = Charset[index]
+	}
+	return string(genome)
+}
+
+// calculateFitness computes how close an individual is to the target string
+func calculateFitness(genome string) float64 {
+	matches := 0
+	for i, char := range genome {
+		if char == rune(Target[i]) {
+			matches++
+		}
+	}
+	return float64(matches) / float64(len(Target))
+}
+
+// findBestIndividual finds the individual with the highest fitness in the population
+func findBestIndividual(population []Individual) Individual {
+	best := population[0]
+	for _, individual := range population {
+		if individual.Fitness > best.Fitness {
+			best = individual
+		}
+	}
+	return best
+}
+
+// generateNextGeneration produces the next generation using selection, crossover, and mutation
+func generateNextGeneration(population []Individual, ei *entropy.Instance) []Individual {
+	//println("---generate next generation")
+	nextGeneration := make([]Individual, len(population))
+
+	for i := 0; i < len(population); i++ {
+		// Selection: Pick two parents
+		parent1 := selectIndividual(population, ei)
+		parent2 := selectIndividual(population, ei)
+
+		// Crossover: Create a child
+		childGenome := crossover(parent1.Genome, parent2.Genome, ei)
+
+		// Mutation: Introduce random changes
+		childGenome = mutate(childGenome, ei)
+
+		// Add child to the next generation
+		nextGeneration[i] = Individual{
+			Genome: childGenome,
+		}
+	}
+
+	return nextGeneration
+}
+
+// selectIndividual selects an individual from the population based on fitness (roulette wheel selection)
+func selectIndividual(population []Individual, ei *entropy.Instance) Individual {
+	totalFitness := 0.0
+	for _, individual := range population {
+		totalFitness += individual.Fitness
+	}
+	r := (float64(ei.Value()) / float64(math.MaxUint32)) * totalFitness
+
+	cumulative := 0.0
+	for _, individual := range population {
+		cumulative += individual.Fitness
+		if cumulative >= r {
+			return individual
+		}
+	}
+
+	return population[len(population)-1]
+}
+
+// crossover combines two parent genomes to produce a child genome
+func crossover(parent1, parent2 string, ei *entropy.Instance) string {
+	child := make([]rune, len(parent1))
+
+	cutPoint := int(ei.Value()) % len(parent1)
+
+	for i := 0; i < len(parent1); i++ {
+		if i < cutPoint {
+			child[i] = rune(parent1[i])
+		} else {
+			child[i] = rune(parent2[i])
+		}
+	}
+	return string(child)
+}
+
+// mutate introduces random changes to a genome based on the mutation rate
+func mutate(genome string, ei *entropy.Instance) string {
+	mutated := []rune(genome)
+	for i := 0; i < len(mutated); i++ {
+		rv := ei.Value()
+		if float64(rv) < MutationRate {
+			index := rv % uint32(len(Charset))
+			mutated[i] = Charset[index]
+		}
+	}
+	return string(mutated)
+}
diff --git a/examples/gno.land/r/demo/games/ga/z0_filetest.gno b/examples/gno.land/r/demo/games/ga/z0_filetest.gno
new file mode 100644
index 00000000000..fb21f9ab8f0
--- /dev/null
+++ b/examples/gno.land/r/demo/games/ga/z0_filetest.gno
@@ -0,0 +1,16 @@
+package main
+
+import (
+	"gno.land/p/demo/entropy"
+	"gno.land/r/demo/games/ga"
+)
+
+func main() {
+
+	r := entropy.New()
+
+	gen, target := ga.Start(r)
+}
+
+// Output:
+// Target reached!