A generic algorithim mutation

generic mutation.py

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+# Python3 program to create target string, starting from 
+# random string using Genetic Algorithm 
+
+import random 
+
+# Number of individuals in each generation 
+POPULATION_SIZE = 10
+
+# Valid genes 
+GENES = '''abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOP 
+QRSTUVWXYZ 1234567890, .-;:_!"#%&/()=?@${[]}'''
+
+# Target string to be generated 
+TARGET = "I love GeeksforGeeks"
+
+class Individual(object): 
+    ''' 
+    Class representing individual in population 
+    '''
+    def __init__(self, chromosome): 
+        self.chromosome = chromosome 
+        self.fitness = self.cal_fitness() 
+
+    @classmethod
+    def mutated_genes(self): 
+        ''' 
+        Create random genes for mutation 
+        '''
+        global GENES 
+        gene = random.choice(GENES) 
+        return gene 
+
+    @classmethod
+    def create_gnome(self): 
+        ''' 
+        Create chromosome or string of genes 
+        '''
+        global TARGET 
+        gnome_len = len(TARGET) 
+        return [self.mutated_genes() for _ in range(gnome_len)] 
+
+    def mate(self, par2): 
+        ''' 
+        Perform mating and produce new offspring 
+        '''
+
+        # Chromosome for offspring 
+        child_chromosome = [] 
+        for gp1, gp2 in zip(self.chromosome, par2.chromosome):     
+
+            # Random probability 
+            prob = random.random() 
+
+            # If prob is less than 0.45, insert gene 
+            # from parent 1 
+            if prob < 0.45: 
+                child_chromosome.append(gp1) 
+
+            # If prob is between 0.45 and 0.90, insert 
+            # gene from parent 2 
+            elif prob < 0.90: 
+                child_chromosome.append(gp2) 
+
+            # Otherwise insert random gene (mutate), 
+            # for maintaining diversity 
+            else: 
+                child_chromosome.append(self.mutated_genes()) 
+
+        # Create new Individual (offspring) using 
+        # generated chromosome for offspring 
+        return Individual(child_chromosome) 
+
+    def cal_fitness(self): 
+        ''' 
+        Calculate fitness score, it is the number of 
+        characters in string which differ from target 
+        string. 
+        '''
+        global TARGET 
+        fitness = 0
+        for gs, gt in zip(self.chromosome, TARGET): 
+            if gs != gt: 
+                fitness += 1
+        return fitness 
+
+# Driver code 
+def main(): 
+    global POPULATION_SIZE 
+
+    # Current generation 
+    generation = 1
+
+    found = False
+    population = [] 
+
+    # Create initial population 
+    for _ in range(POPULATION_SIZE): 
+        gnome = Individual.create_gnome()  # Fixed this
+        population.append(Individual(gnome)) 
+
+    while not found: 
+
+        # Sort the population in increasing order of fitness score 
+        population = sorted(population, key = lambda x:x.fitness) 
+
+        # If the individual having the lowest fitness score is 0, 
+        # we have reached the target 
+        if population[0].fitness <= 0: 
+            found = True
+            break
+
+        # Otherwise, generate new offsprings for new generation 
+        new_generation = [] 
+
+        # Perform Elitism, that means 10% of fittest population 
+        # goes to the next generation 
+        s = int((10 * POPULATION_SIZE) / 100) 
+        new_generation.extend(population[:s]) 
+
+        # From 50% of the fittest population, individuals 
+        # will mate to produce offspring 
+        s = int((90 * POPULATION_SIZE) / 100) 
+        for _ in range(s): 
+            parent1 = random.choice(population[:50]) 
+            parent2 = random.choice(population[:50]) 
+            child = parent1.mate(parent2) 
+            new_generation.append(child) 
+
+        population = new_generation 
+
+        # Print current generation details 
+        print("Generation: {}\tString: {}\tFitness: {}".format(
+            generation, 
+            "".join(population[0].chromosome), 
+            population[0].fitness)) 
+
+        generation += 1
+
+    # Print final result
+    print("Generation: {}\tString: {}\tFitness: {}".format(
+        generation, 
+        "".join(population[0].chromosome), 
+        population[0].fitness)) 
+
+if __name__ == '__main__': 
+    main()