Hi!

First and foremost, I want to express my gratitude for this incredible library and its clear documentation. Congratulations@ahmedfgad on the excellent work.
Thanks!

Now, version 3.3.0 is released... 👀 maybe we already have the variable chromossome feature? 👀

If not, my approach would simply have a big lengthgene_space and consider some special values (like-1) as the absent of that gene (I'm using "gene" as sinonym of "chromossome" here, but I know there's a small conceptual difference).

In my specific case, my optimization involves positioning weights in certain positions (which must be integers in a given range).
I don't know before hand how many weights I need, but I know it won't be bigger than, let's say, 5 weights. So I could have:

gene_space = [range(-1, max_weight),range(-1, max_weight),range(-1, max_weight),range(-1, max_weight),range(-1, max_weight),]

(I know I could just use gene_space=range(-1, max_weight),, but was to clarify that each individual would have 5 genes).

Then, on my fitness function I would have something like:

import pygadfrom typing import Tuple, LiteralWeightPositionAsNone = Literal[-1]MaxWeightPosition = Literal[5]WeightPosition = Literal[WeightPositionAsNone,                         0, 1, 2, 3, 4, MaxWeightPosition]Solution = Tuple[    WeightPosition,    WeightPosition,    WeightPosition,    WeightPosition,    WeightPosition,]def fitness_func(        ga_instance: pygad.GA,        solution: Solution,        solution_idx: int):    filtered_solution = [x for x in solution if x != -1]    # Calculate fitness solution for the problem    # desconsidering the weights that are not present    fitness = sum(filtered_solution)    return fitness

Does this seem like a valid approach?

This is absolutely a good idea. Still the feature is not fully merged. It is a major change.