Decoding Genetic Algorithms In Python

Genetic algorithms (GA) are one of the fascinating concepts in artificial intelligence and machine learning domains. They are grounded in the theory of natural evolution, which brings out the survival of the fittest. GAs are employed in optimization and search problems due to their capability to find optimal or near-optimal solutions.

Basic Concepts of Genetic Algorithms:

At the heart of GAs are following concepts:

1. Selection: Preference for the propagation of better solutions in the population.
2. CrossOver (Mating): Combining the DNA of parents to produce offspring.
3. Mutation: To maintain diversity within the population, changes are introduced at random.

Python Implementation of Genetic Algorithm

Let me guide you on how to implement a fundamental Genetic Algorithm in Python. We will use GA to find the maximum point in a function y = x^2.

Requirements: numpy

import numpy as np

'Individual' Class Creation

First, we create the 'Individual' class. An 'Individual' represents a solution in the population. Since our solution is a real number ranging from -10 to +10, we represent it as a binary string with a precision of up to 4 decimal places.

class Individual(object):

    def __init__(self, chromosome):
        self.chromosome = chromosome 
        self.fitness = self.cal_fitness()

    @classmethod
    def mutated_genes(self):
        #Creating random genes for mutation 
        global geneSet
        gene = int(random()*geneSet)
        return gene

    @classmethod
    def create_gnome(self):
        # create chromosome or string of genes
        global length
        gnome_len = length
        return [self.mutated_genes() for _ in range(gnome_len)]

    def mate(self, par2):
        # perform mating and produce new offspring
        ...
        
    def cal_fitness(self):
        # calculate f(x) for chromosome x
        ...

Initializing Population and Running GA

Initialize the population and set fitness function, mutation rate etc. Then, we keep selecting, mating and mutating individuals until we either find the perfect individual or reach a maximum number of generations.

def main():
    global POPULATION_SIZE

    generation = 1

    population = []

    for _ in range(POPULATION_SIZE):
            gnome = Individual.create_gnome()
            population.append(Individual(gnome))

    while not stop:
        # to be continued
        ...

if __name__ == '__main__':
	main()

The code shown above is a simplified representation of a genetic algorithm. Actual GA implementations can have more advanced concepts and functionalities like elitism, multiple crossover and mutation types, and dynamic mutation rate to name a few. GAs can be an efficient method for solving a wide array of optimization problems not limited to software and can even find applications in areas such as genetic research, economics, and cryptography.