A Simple Genetic Algorithm in Python for finding the best way to represent a number as a (limited) polynomial.

Python GE

from random import randint
#GE

#Constants
gens = 150
popNum = 30
mutRate = 20  # Rate of mutation = 1/mutRate
goal = 97
chromosomeLength = 24 # Must be a multiple of 5 - 1. Example: 14, 19, 24, 29, 34

#Init Variables
population = [[randint(0,1) for gene in range(0,chromosomeLength)] for x in range(0,popNum)]

#Math Funcs
def add (x,y):
    return x + y

def sub(x,y):
    return x - y

def mul(x,y):
    return float(x) * float(y)

def div(x,y):
    if y == 0: y = 1 #prevents from dividebyzero OH SHIT
    return float(x) / float(y)

def getVal(val):
    newVal = ""
    for n in val:
        newVal = newVal + str(n)
    if newVal == "0000":
        return 1
    elif newVal == "0001":
        return 2
    elif newVal == "0010":
        return 3
    elif newVal == "0011":
        return 4
    elif newVal == "0100":
        return 5
    elif newVal == "0101":
        return 6
    elif newVal == "0110":
        return 7
    elif newVal == "0111":
        return 8
    elif newVal == "1000":
        return 9
    elif newVal == "1001":
        return 10
    elif newVal == "1010":
        return 11
    elif newVal == "1011":
        return 12
    elif newVal == "1100":
        return 13
    elif newVal == "1101":
        return 14
    elif newVal == "1110":
        return 15
    elif newVal == "1111":
        return 16
    else:
        return 1

#Fit Func
def evaluate (animal,willPrint):
    stack = []
    toPrint = ""
    while 1:
        if len(stack) < 2:
            stack.append(getVal(animal[0:4]))
            toPrint = toPrint + str(getVal(animal[0:4]))
            animal = animal[4:]
        else:
            if animal[0] == 0:
                stack.append(mul(stack.pop(0),stack.pop(0)))
                toPrint = toPrint + "*"
            else:
                stack.append(div(stack.pop(0),stack.pop(0)))
                toPrint = toPrint + "/"
            animal = animal [1:]
        if len(animal) == 0:
            break
        toPrint = toPrint + " "
    if willPrint == True:
        print (toPrint)
    return stack[0]

def fitness (num):
    return abs(num - goal)

#Main
bestOfAllFit = population[0]
greatestFit = population[0]
for gen in range(0,gens):
    print ("Number: " + str(evaluate(greatestFit,True)) )
    print ("Distance from Goal " + str(fitness(evaluate(greatestFit,False))) )
    greatestFit = population[0]
    #Get new Greatest Fitness
    for animal in population:
        if fitness(evaluate(animal,False)) < fitness(evaluate(greatestFit,False)):
            greatestFit = animal
    if fitness(evaluate(greatestFit,False)) < fitness(evaluate(bestOfAllFit,False)):
        bestOfAllFit = greatestFit
        print ("\n---IMPORTANT TRANSITIONAL FORM---\n")
        
    #Breed Populations
    for animal in range(0,len(population)):
        newAnimal = []
        for gene in range(0,len(population[animal])):
            if randint(0,mutRate) == 0:
                newAnimal.append(randint(0,1))
            else:
                if randint(0,1) == 0:
                    newAnimal.append(greatestFit[gene])
                else:
                    newAnimal.append(population[animal][gene])
        population[animal] = newAnimal
print ("\n\n\n\nBest Solution: ")
print ("Number: " + str(evaluate(greatestFit,True)) )
print ("Distance from Goal: " + str(fitness(evaluate(greatestFit,False))) )