Genetic-Algorithm | C # ile genetik algoritma uygulaması

Im trying to recreate this code: https://github.com/Code-Bullet/Smart-Dots-Genetic-Algorithm-Tutorial/tree/master/BestTutorialEver , but in python, and it doesn't work, it keeps mutating the best dot and every generation starts with less dots. Here is the code (i use pygame for graphics):

Brain class:

I'm experimenting with a genetic search algorithm and after building the initial population at random, and then selecting the top two fittest entries, I need to 'mate' them (with some random mutation) to create 64 'children'. The crossover part, explained here:

https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3

seems easy to follow, but I can't seem to figure out how to implement it in Python. How can I implement this crossover of two integers?

I'm writing Custom GA functions in MATLAB for mixed integer problem. More about my problem here and here.

After Roulette Wheel selection is over, Matlab breaks with error:

I'm writing an genetic algorithm for finding coefficients having given X, Y points. The principle of operation is described on this page - https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3

I have problem, because sometimes after mutation or crossover my double value is NaN.

I've tried do this using byte[] and BitArray, but in both approaches I have the same result.

Converting double <-> BitArray:

I read the tutorial on TutorialsPoint and this question and answer on StackOverflow. However, I still do not understand the meaning of Crossover Probability in the Parent Selection and Crossover process of a genetic algorithm.

Say I have a population of size 100 and the crossover probability is 0.9. What does it mean? Do I:

• select precisely 10 parents (since 90 % of offsprings shall be made by crossover), or
• run a RNG 100 times and for each time the 0.9 probability fails, I select a parent?

Then, the parents are somehow crossed over and some individuals mutate. Does the population need to have exactly 100 members at this point, or there is an additional selection of which individuals make it to the next generation?

this question is a follow-on to answer of this question about python deap genetic algorithm library: How to add elimination mechanism in Python genetic algorithm based on DEAP

using reference code from deap github: https://github.com/DEAP/deap/blob/master/examples/ga/onemax.py

line 112 while max(fits) < 100 and g < 1000: #from onemax.py

on the deap github example 'onemax_mp.py': https://github.com/DEAP/deap/blob/master/examples/ga/onemax_mp.py

how do i add a max(or min) condition similar to max(fits) < 100 in the onemax_mp.py?

if i do add this condition is this condition applied to each process in the entire multi-process pool of processes? if one process meets the end condition are the other processes halted?
right now it seems that i can only control the number of generations:

https://github.com/DEAP/deap/blob/master/examples/ga/onemax_mp.py

line 40

algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=40, stats=stats, halloffame=hof) #ngen=40 means calculate 40 generations

i am new to stackoverflow, please let me know if i need to edit this question to fit forum rules

I came across this notation today as going through this tutorial here:https://towardsdatascience.com/genetic-algorithm-implementation-in-python-5ab67bb124a6

I am trying to make work a piece of code in Python, composed of four modules.

When running I get the following error in the module optimizer_Neuroevolution: "line 149, in evolve retain_length = int(len(graded)*self.retain) TypeError: unsupported operand type(s) for *: 'int' and 'type'

The modules are as follow:

First module main_Neuroevolution:

I couldn't figure out the following problem: if I may, I'd like to give you right away an example
Imagine, you work with marketing data and you came up with a good regression model, predicting the "reach" of a certain campaign. All fine and dandy. Data Scientist Job done.
But wait. We can do more.
My question to you is:
Assuming that we have a good model, how can we optimize the input vector ( = marketing campaign) to get the best possible "reach" ( = predictor / goal to optimize)?
I was googling like crazy, but couldn't find any good approach (I am not talking about any hypterparameter optimization). The best approach I found so far is a genetic algorithm... example here and here
Or - a brute force approach - calculate an enormous grid with tons of possible input vectors and then check, which one is the best (straight forward) - but that would be computational expensive.

I would love to hear your opinion on this. Any advice on which topics I should check out?