Python Genetic Algorithm Implementation

Question 1

I've made this to pick the LQR parameters for my self balancing robots simulation. So to break down what I have done,

Randomly Create Population

I create a random population. The population is a list of lists having 4 parameters each of which is randomly generated. But I have applied bounds for them using the values I got from my manual tuning for my simulation.
```
 def population_create(p_size,geno_size,bounds): p = [ random_param(geno_size,bounds) for i in range(p_size)] return pdef random_param(g,b):     return [random.uniform(b[i][0],b[i][1]) for i in range(g)]
```

Try it on Robot and Evaluate

2.Then I evaluate each parameter to see how much the wheels have rotated and how much the robot is inclined after one minute.

fitness = encoder_readings + tilt

So the closer the fitness is to zero the better the robot balances.This is done for the whole population

Create next generation

3.Then I make the next generation with Mutation, Crossover and passing healthy individuals directly.

def population_reproduce(p,fitness):    size_p = len(p)    new_p = []        dataframe = pd.DataFrame({"Param":p,"Fitness":fitness})    dataframe = dataframe.sort_values(['Fitness'])    dataframe = dataframe.reset_index(drop=True)        sorted_p = dataframe['Param'].tolist()    elite_part = round(ELITE_PART*size_p)    new_p = new_p + sorted_p[:elite_part]    for i in range(size_p-elite_part):        mom = p[random.randint(0,size_p-1)]        dad = p[random.randint(0,size_p-1)]        child = crossover(mom,dad)        child = mutate(child)        new_p.append(child)        return new_p

def crossover(p1,p2):        crossover = []    locii = [random.randint(0,8) for _ in range(len(p1))]        for i in range(len(p1)):        if locii[i]>4:            crossover.append(p2[i])        else:            crossover.append(p1[i])            return crossoverdef mutate(c):    size = len(c)    for i in range(size):        if random.random()< MUTATION_PROBABILITY:            c[i] += random.gauss(c[i]/50,MUTATION_DEVIATION)                return c

This continues for some generations.

These are the links to the full codes.

Genetic Algorithm Supervisor

Population Script

Robot Controller

I would very much appreciate if you could take a look and let me know if this is a correct implementation of a genetic algorithm.

One observation I made is that the fitness doesn't converge to a lower value after each iteration. It randomly goes up and down. Could this be because of some inconsistency in my stimulation on Webots or is it a mistake in my code thats causing this?

Question 2

Hey there, I'm under the impression this community is for reviewing code that is working, and not necessarily to check the correctness of your code :)

Question 3

I'm sorry if this isn't the place I should be posting this. This code does work. I'm not sure how much I could call this a genetic algorithm? Because most genetic algorithms I've seen tend to converge(get fitter over each iteration).Though mine works it doesn't necessarily converge like the others. So I was expecting if someone could help me improve this like giving me some ideas. Thanks @RGS

Question 4

On the style of the code:

While I'm not saying the code is or isn't correct, there's some small tweaks you could make it to improve it's legibility:

Spacing

Write code like you would write English (in some sense :P). After commas you needs spaces, so do

def crossover(p1, p2): instead ofdef crossover(p1,p2):;
random.uniform(b[i][0], b[i][1]) instead ofrandom.uniform(b[i][0],b[i][1])
same thing around binary ops:if locii[i] > 4: instead ofif locii[i]>4:
etc.

List comprehensions often do not need a leading space, so you can go with

p = [random_param(geno_size, bounds) for i in range(p_size)]

instead of

p = [ random_param(geno_size,bounds) for i in range(p_size)]

crossover

You have written:

def crossover(p1,p2):        crossover = []    locii = [random.randint(0,8) for _ in range(len(p1))]        for i in range(len(p1)):        if locii[i]>4:            crossover.append(p2[i])        else:            crossover.append(p1[i])            return crossover

Consider this alternative definition:

def crossover(p1, p2):        crossover = []    for p1_elem, p2_elem in zip(p1, p2):        if random.randint(0,8) > 4:            crossover.append(p2_elem)        else:            crossover.append(p1_elem)            return crossover

Notice I don't precompute alllocii, although you could do that, of course. Also note the usage ofzip which means I can traverse bothp1 andp2, instead of having to use indices to later grab the values. (You can read more aboutzip and find other tips and trickshere.

random_param

In line with one of the things I showed above, instead of doing

def random_param(g,b):     return [random.uniform(b[i][0],b[i][1]) for i in range(g)]

you could iterate directly overb:

def random_param(g, b):     return [random.uniform(bs[0], bs[1]) for bs in b]

(More on better iterating in Pythonhere.)

Then, take that and unpackbs with* instead of with indices:

def random_param(g, b):     return [random.uniform(*bounds) for bounds in b]

RGS 1,0785 silver badges18 bronze badges · Answer 1 · 2021-04-13 10:22:41Z

On the style of the code:

While I'm not saying the code is or isn't correct, there's some small tweaks you could make it to improve it's legibility:

Spacing

Write code like you would write English (in some sense :P). After commas you needs spaces, so do

def crossover(p1, p2): instead ofdef crossover(p1,p2):;
random.uniform(b[i][0], b[i][1]) instead ofrandom.uniform(b[i][0],b[i][1])
same thing around binary ops:if locii[i] > 4: instead ofif locii[i]>4:
etc.

List comprehensions often do not need a leading space, so you can go with

p = [random_param(geno_size, bounds) for i in range(p_size)]

instead of

p = [ random_param(geno_size,bounds) for i in range(p_size)]

crossover

You have written:

def crossover(p1,p2):        crossover = []    locii = [random.randint(0,8) for _ in range(len(p1))]        for i in range(len(p1)):        if locii[i]>4:            crossover.append(p2[i])        else:            crossover.append(p1[i])            return crossover

Consider this alternative definition:

def crossover(p1, p2):        crossover = []    for p1_elem, p2_elem in zip(p1, p2):        if random.randint(0,8) > 4:            crossover.append(p2_elem)        else:            crossover.append(p1_elem)            return crossover

Notice I don't precompute alllocii, although you could do that, of course. Also note the usage ofzip which means I can traverse bothp1 andp2, instead of having to use indices to later grab the values. (You can read more aboutzip and find other tips and trickshere.

random_param

In line with one of the things I showed above, instead of doing

def random_param(g,b):     return [random.uniform(b[i][0],b[i][1]) for i in range(g)]

you could iterate directly overb:

def random_param(g, b):     return [random.uniform(bs[0], bs[1]) for bs in b]

(More on better iterating in Pythonhere.)

Then, take that and unpackbs with* instead of with indices:

def random_param(g, b):     return [random.uniform(*bounds) for bounds in b]

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