Is it possible to implement elitism?

[ELC]

I wrote this function and used it as a callback to attemp elitist selection without success:

best_chromosome = None

def save_best(population):
    global best_chromosome
    
    population_best = population.current_best
    
    if best_chromosome is None or population_best.fitness > best_chromosome.fitness:
        best_chromosome = deepcopy(population_best)

Is there a way to do it?

[koaning]

I think this is where the callback step can really make a difference.

https://github.com/godatadriven/evol/blob/master/evol/evolution.py#L189-L204

[ELC]

Hi @koaning, I use the callback step with the function I showed but someone the population does not have the behaviour I want

[koaning]

Is there a minimum viable example that you can share? Without one it is very hard for me to understand the missing behavior.

[ELC]

Thanks for the quick reply!

This an example, it is based on the README example so it doesn't add too much different behaviour. Below is the history of best fitness, which I would have expected to be monotonically increasing but it wasn't.

import random
from evol import Population, Evolution
from copy import deepcopy

random.seed(30)

def random_start():
    """
    This function generates a random (x,y) coordinate
    """
    return (random.random() - 0.5) * 20, (random.random() - 0.5) * 20

def func_to_optimise(xy):
    """
    This is the function we want to optimise (maximize)
    """
    x, y = xy
    return -(1-x)**2 - 2*(2-x**2)**2

def pick_random_parents(pop):
    """
    This is how we are going to select parents from the population
    """
    mom = random.choice(pop)
    dad = random.choice(pop)
    return mom, dad

def make_child(mom, dad):
    """
    This function describes how two candidates combine into a new candidate
    Note that the output is a tuple, just like the output of `random_start`
    We leave it to the developer to ensure that chromosomes are of the same type
    """
    child_x = (mom[0] + dad[0])/2
    child_y = (mom[1] + dad[1])/2
    return child_x, child_y

def add_noise(chromosome, sigma):
    """
    This is a function that will add some noise to the chromosome.
    """
    new_x = chromosome[0] + (random.random()-0.5) * sigma
    new_y = chromosome[1] + (random.random()-0.5) * sigma
    return new_x, new_y


best_chromosome = None
fitness_history = []

def save_best(population):
    global best_chromosome

    if not population.is_evaluated:
        population.evaluate()
        
    population_best = population.current_best

    if best_chromosome is None or population_best.fitness > best_chromosome.fitness:
        best_chromosome = deepcopy(population_best)

    amount = len(population.individuals)
    index = np.random.randint(0, amount)
    population.individuals[index] = best_chromosome
    
    fitness_history.append(population.current_best.fitness)

# We start by defining a population with candidates.
pop = Population(chromosomes=[random_start() for _ in range(10)],
                 eval_function=func_to_optimise, maximize=True)

# We define a sequence of steps to change these candidates
evo1 = (Evolution()
       .survive(fraction=0.5)
       .breed(parent_picker=pick_random_parents, combiner=make_child)
       .mutate(mutate_function=add_noise, sigma=1.5)
       .callback(save_best)
       )

pop = pop.evolve(evo1, n=50)

with plt.style.context("bmh"):
    plt.figure(figsize=(16,5))
    plt.plot(fitness_history)

First 100 steps:

[koaning]

Your example isn't complete. You didn't add the code that handles the actual logging of stats you see in your chart or the generate_chromosome function to generate the candidates. I cannot reproduce your code so I cannot confirm/deny if there's a bug in your code.

[ELC]

You are right, I am sorry.

I edited the message and use the README code to build a minimal self-contained example.

I am reporting the population.current_best.fitness for the generation fitness, and this is the metric I would like to keep monotonically increasing

[koaning]

I have a hint of what is happening. You're logging the best candidate per generation but you're still applying a mutate. This mutate is what is messing with your expectation. Here's the same charts when I increase/decrease the noise added.

Same Run with sigma=10

Same Run with sigma=0.01

You might want to adapt the mutate function such that it doesn't change if the parent is the best candidate.

[ELC]

I know but the idea is behind elistism is that we save the best chromosome from the population before selection and mutation and then we add it to the population after mutation.

The behaviour I want is to be able to make some chromosome jump the breed and mutate steps even if they occur 100% of the time. This is to ensure that at the end of the run, the best chromosome is the historical best.

And this should happen independently of how the breed and mutation was configured.

Here is an illustration of what I am looking for:

[koaning]

Mhmm ... now I understand.

I'll need to ponder this one but I think you're right that this isn't easily implemented with the current API. Maybe a "concat" verb makes sense but I'll need to give it a think.

It should also be said; I no longer work for GoDataDriven and I haven't touched this library in ... years? Just to check @rogiervandergeer you still watching this repo?

[koaning]

Would it be sensible to change the mutate function to not change the chromosomes of the best performing candidates.

[ELC]

Modifying the breed and mutate methods of population should work for most cases (cases where callbacks modify the population are exceptions).

breed should also be modified because when luck=True it is not guaranteed that the best performing candidate will be kept.

[ELC]

After trying several things, I think I found a solution:

I was using

population.individuals[index] = best_chromosome,

and I should have used

population.individuals[index] = deepcopy(best_chromosome)

I will do some tests and update the issue tomorrow

[rogiervandergeer]

It should also be said; I no longer work for GoDataDriven and I haven't touched this library in ... years? Just to check @rogiervandergeer you still watching this repo?

Of course I am! And don't let a mutation in the value for current_employer stop you from working on Evol!

Modifying the breed and mutate methods of population should work for most cases (cases where callbacks modify the population are exceptions).

breed should also be modified because when luck=True it is not guaranteed that the best performing candidate will be kept.

In #159 I've added a flag to let mutate behave elitist. For the survive function, I think setting luck=True is so contradictory to elitism that I'm happy with the current situation. @ELC would this work for you?

[koaning]

I just pushed version 0.5.2 which should have this elitism feature. @ELC let us know if it works.

[GiliardGodoi]

Hi guys!

I don't know if this is the right place for my thoughts, but here we go!

I do know that 'evol' has a fancy way of defining an evolutionary algorithm's pipeline, process, whatever...
Like the example above, we don't code directly with 'Evolution Step' classes.
Instead, 'Evolution Step' objects are appended onto the 'Evolution' chain, and the Population's method 'evolve' applies the 'Evolution Step' objects in the population.
On the other hand, it calls some method on the 'Population' class like breed, mutate or do some operation directly in the population methods like filter and map.

And I'm aware of the great flexibility and simplicity of implementing our custom mate function, chromosome evaluation function, picker and combiner function, etc., and passes it to the evolutionary pipeline.

However, I notice that those methods in the 'Population' class are getting more and more complicated.
We might be adding some behaviors in these methods, but they are not necessary all the time.
Additionally, we add some 'if ... else' clauses to achieve these different behaviors.
One side effect is that parameters may be necessary in some cases, but they may not be in others. When do we need to set them? It is not clear.

So I wondered -- and this would be a significant change in the framework's architecture -- if the 'Evolution Step' classes implemented these different kinds of strategies. For instance, we're going to have different mutation strategies: simple mutation, elitism mutation, and so on. We can compose these different strategies in uncountable ways.

In this circumstance, this new object would implement the evolution step and not merely call a method from a class like 'Population.'

Again, this is just a thought.