diff --git a/genetic_algorithm/basic_string.py b/genetic_algorithm/basic_string.py
new file mode 100644
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+++ b/genetic_algorithm/basic_string.py
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+"""
+Simple multithreaded algorithm to show how the 4 phases of a genetic algorithm works
+(Evaluation, Selection, Crossover and Mutation)
+https://en.wikipedia.org/wiki/Genetic_algorithm
+Author: D4rkia
+"""
+
+import random
+from typing import List, Tuple
+
+# Maximum size of the population.  bigger could be faster but is more memory expensive
+N_POPULATION = 200
+# Number of elements selected in every generation for evolution the selection takes
+# place from the best to the worst of that generation must be smaller than N_POPULATION
+N_SELECTED = 50
+# Probability that an element of a generation can mutate changing one of its genes this
+# guarantees that all genes will be used during evolution
+MUTATION_PROBABILITY = 0.4
+# just a seed to improve randomness required by the algorithm
+random.seed(random.randint(0, 1000))
+
+
+def basic(target: str, genes: List[str], debug: bool = True) -> Tuple[int, int, str]:
+    """
+    Verify that the target contains no genes besides the ones inside genes variable.
+
+    >>> from string import ascii_lowercase
+    >>> basic("doctest", ascii_lowercase, debug=False)[2]
+    'doctest'
+    >>> genes = list(ascii_lowercase)
+    >>> genes.remove("e")
+    >>> basic("test", genes)
+    Traceback (most recent call last):
+    ...
+    ValueError: ['e'] is not in genes list, evolution cannot converge
+    >>> genes.remove("s")
+    >>> basic("test", genes)
+    Traceback (most recent call last):
+    ...
+    ValueError: ['e', 's'] is not in genes list, evolution cannot converge
+    >>> genes.remove("t")
+    >>> basic("test", genes)
+    Traceback (most recent call last):
+    ...
+    ValueError: ['e', 's', 't'] is not in genes list, evolution cannot converge
+    """
+
+    # Verify if N_POPULATION is bigger than N_SELECTED
+    if N_POPULATION < N_SELECTED:
+        raise ValueError(f"{N_POPULATION} must be bigger than {N_SELECTED}")
+    # Verify that the target contains no genes besides the ones inside genes variable.
+    not_in_genes_list = sorted({c for c in target if c not in genes})
+    if not_in_genes_list:
+        raise ValueError(
+            f"{not_in_genes_list} is not in genes list, evolution cannot converge"
+        )
+
+    # Generate random starting population
+    population = []
+    for _ in range(N_POPULATION):
+        population.append("".join([random.choice(genes) for i in range(len(target))]))
+
+    # Just some logs to know what the algorithms is doing
+    generation, total_population = 0, 0
+
+    # This loop will end when we will find a perfect match for our target
+    while True:
+        generation += 1
+        total_population += len(population)
+
+        # Random population created now it's time to evaluate
+        def evaluate(item: str, main_target: str = target) -> Tuple[str, float]:
+            """
+            Evaluate how similar the item is with the target by just
+            counting each char in the right position
+            >>> evaluate("Helxo Worlx", Hello World)
+            ["Helxo Worlx", 9]
+            """
+            score = len(
+                [g for position, g in enumerate(item) if g == main_target[position]]
+            )
+            return (item, float(score))
+
+        # Adding a bit of concurrency can make everything faster,
+        #
+        # import concurrent.futures
+        # population_score: List[Tuple[str, float]] = []
+        # with concurrent.futures.ThreadPoolExecutor(
+        #                                   max_workers=NUM_WORKERS) as executor:
+        #     futures = {executor.submit(evaluate, item) for item in population}
+        #     concurrent.futures.wait(futures)
+        #     population_score = [item.result() for item in futures]
+        #
+        # but with a simple algorithm like this will probably be slower
+        # we just need to call evaluate for every item inside population
+        population_score = [evaluate(item) for item in population]
+
+        # Check if there is a matching evolution
+        population_score = sorted(population_score, key=lambda x: x[1], reverse=True)
+        if population_score[0][0] == target:
+            return (generation, total_population, population_score[0][0])
+
+        # Print the Best result every 10 generation
+        # just to know that the algorithm is working
+        if debug and generation % 10 == 0:
+            print(
+                f"\nGeneration: {generation}"
+                f"\nTotal Population:{total_population}"
+                f"\nBest score: {population_score[0][1]}"
+                f"\nBest string: {population_score[0][0]}"
+            )
+
+        # Flush the old population keeping some of the best evolutions
+        # Keeping this avoid regression of evolution
+        population_best = population[: int(N_POPULATION / 3)]
+        population.clear()
+        population.extend(population_best)
+        # Normalize population score from 0 to 1
+        population_score = [
+            (item, score / len(target)) for item, score in population_score
+        ]
+
+        # Select, Crossover and Mutate a new population
+        def select(parent_1: Tuple[str, float]) -> List[str]:
+            """Select the second parent and generate new population"""
+            pop = []
+            # Generate more child proportionally to the fitness score
+            child_n = int(parent_1[1] * 100) + 1
+            child_n = 10 if child_n >= 10 else child_n
+            for _ in range(child_n):
+                parent_2 = population_score[random.randint(0, N_SELECTED)][0]
+                child_1, child_2 = crossover(parent_1[0], parent_2)
+                # Append new string to the population list
+                pop.append(mutate(child_1))
+                pop.append(mutate(child_2))
+            return pop
+
+        def crossover(parent_1: str, parent_2: str) -> Tuple[str, str]:
+            """Slice and combine two string in a random point"""
+            random_slice = random.randint(0, len(parent_1) - 1)
+            child_1 = parent_1[:random_slice] + parent_2[random_slice:]
+            child_2 = parent_2[:random_slice] + parent_1[random_slice:]
+            return (child_1, child_2)
+
+        def mutate(child: str) -> str:
+            """Mutate a random gene of a child with another one from the list"""
+            child_list = list(child)
+            if random.uniform(0, 1) < MUTATION_PROBABILITY:
+                child_list[random.randint(0, len(child)) - 1] = random.choice(genes)
+            return "".join(child_list)
+
+        # This is Selection
+        for i in range(N_SELECTED):
+            population.extend(select(population_score[int(i)]))
+            # Check if the population has already reached the maximum value and if so,
+            # break the cycle.  if this check is disabled the algorithm will take
+            # forever to compute large strings but will also calculate small string in
+            # a lot fewer generations
+            if len(population) > N_POPULATION:
+                break
+
+
+if __name__ == "__main__":
+    target_str = (
+        "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
+    )
+    genes_list = list(
+        " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
+        "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
+    )
+    print(
+        "\nGeneration: %s\nTotal Population: %s\nTarget: %s"
+        % basic(target_str, genes_list)
+    )