This document describes a simple genetic algorithm implemented in Python to evolve a population of strings toward a specified target string through processes of selection, crossover, mutation, and replacement. It outlines initialization parameters, population generation, and the main loop iterations until the target is reached, including details on fitness evaluation and generation printing. The algorithm aims to create strings closely matching the target by utilizing a predefined set of genes.

1. Genetic Algorithm in
Python Programming

2. Genetic Algorithm
This code implements a simple genetic algorithm to evolve
a population of strings towards a target string. The target string is
specified as 'TARGET', and the genetic algorithm aims to
generate a population of strings that closely match the target
string through iterations of selection, crossover, mutation, and
replacement.

3. Initialization:
•POP_SIZE: Number of individuals in the population.
•MUT_RATE: Mutation rate, indicating the probability of a mutation occurring.
•TARGET: The target string that the algorithm aims to evolve towards.
•GENES: The set of characters that can be used to form individuals in the
population.

4. Initialization of Population:
initialize_pop(TARGET): Generates an initial population of
strings, each having the same length as the target string. The
characters of the strings are randomly chosen from the set of
genes.

5. Crossover
crossover(selected_chromo, CHROMO_LEN, population):
Performs crossover/mating between selected individuals from the
current population to create a new population

6. Mutation
mutate(offspring, MUT_RATE): Introduces random mutations in
the offspring with a probability specified by the mutation rate.

7. Selection
Selection(population, TARGET): Selects the best
individuals from the current population based on
their fitness.

8. Replacement
replace(new_gen, population): Replaces individuals
in the current population with new individuals from the
new generation if they are fitter.

9. Main Genetic Algorithm Loop:
main(POP_SIZE, MUT_RATE, TARGET, GENES): The main loop that
iterates through generations until the target string is found. It involves the
following steps:

10. • Initialize the population.
• Calculate the fitness of the initial population.
• Iterate until the target is found:
• Select the best individuals.
• Mate parents to create a new generation.
• Mutate the new generation.
• Replace the least fit individuals with the new generation.
• Check if the target is found and print information.

11.  The algorithm prints information about each generation, including the
string, generation number, and fitness of the fittest individual. The
process continues until the target string is found.

12. ¼ sir?
YES ¼ POOOOOO 