The code below is based on the “Hello World” toy genetic algorithm that has been dancing around the internet for a few years now. We implement in a few of the scienctific computing and problem solving classes I teach. Its fun because it is so interdisciplinary. A link to the idea can be found here:

https://github.com/jsvazic/GAHelloWorld

Here is my code as we discussed in this year’s (2024) class

import matplotlib.pyplot as plt
import numpy as np


#set target
Target    = "Joeys poopy ducks"
TargetDNA = [ord(each) for each in Target]

#find number of random charcters to choose from
numRands = 255

#specify length of random string to generate
nLetters   = len(Target)

#people in population
nPeople    = 100

#Mating Parent Number
nParents   = 40

#Set the Mutate Rate
MutateRate = 0.3

#Number of Trials
ntrials    = 1000

pop        = []
#generate random string
for ii in range(0,nPeople):
    DNA   = np.random.randint(low=0,high=numRands,size=(nLetters))

    pop.append(DNA)



for qq in range(0,ntrials):

    # Determine how fit everyone is
    fitness    = np.zeros(nPeople)
    cc         = 0
    for DNA in pop:    
        
        #fitness
        diff = DNA - TargetDNA
        res  = np.sum(np.abs(diff))
        fitness[cc] = res
        
        cc   = cc + 1
          
    # Select the parents for the next generation
    indices = np.argsort(fitness)
    parent  = []
    for ii in range(0,nParents):
        MyIndex = indices[ii]
        #print (MyIndex)
        peep    = pop[MyIndex]
        parent.append(peep)
        
    
    # Mate Parents
    newpop = parent
    for ii in range(nParents,nPeople):
        peep1 = parent[np.random.randint(nParents)]
        peep2 = parent[np.random.randint(nParents)]
        mark  = np.random.randint(nLetters)
        
        DNAL  = peep1[0:mark]
        DNAR  = peep2[mark:nLetters]
        
        baby  = np.hstack((DNAL,DNAR)) 
        
        #Mutate
        while np.random.rand() < MutateRate:
            baby[np.random.randint(nLetters)] = np.random.randint(numRands)
        
        newpop.append(baby)
        
        
        
    # Show Best
    DNABest = parent[0]
    peep    = ''.join(chr(i) for i in DNABest)
    print (qq, peep)
    
    pop     = newpop