Genetic-Algorithm | computer science and operations research | Machine Learning library
kandi X-RAY | Genetic-Algorithm Summary
kandi X-RAY | Genetic-Algorithm Summary
In computer science and operations research, a genetic algorithm is a metaheuristic inspired by process of natural selection that belongs to the larger class of evolutionary algorithms.
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Top functions reviewed by kandi - BETA
- Main loop
- Display info about the population
- Check if the population is better
- Visualize obstacles
- Calculate fitness for population
- Draw cities
- Calculate fitness
- Adds information about the genetic algorithm
- Generate a population of the population
- Finds the best population
- Perform crossover between two DNA sequences
- Generate a random character
- Generate a random character
- Draw a list of cities
- Creates a new image
- Add information about the genetic algorithm
- Repeatedly replicate the population
- Find the most recent population
- Calculate fitness
- Check if the population is valid
Genetic-Algorithm Key Features
Genetic-Algorithm Examples and Code Snippets
Lines of Code : 40License : Permissive (MIT License)
Copydef genetic_algorithm(cities, popsize, elite_size, mutation_rate, generations, plot=True, prn=True):
global route
global progress
population = initial_population(popsize=popsize, cities=cities)
if plot:
animate_progress()
Community Discussions
Trending Discussions on Genetic-Algorithm
QUESTION
Here is the code I am writing: Fake News Detection Google Colab Notebook
The dataset I use: fake_or_real_news
The glove embedding layer: glove.twitter.27B.200d
I've been trying out PyGAD, a python library for genetic algorithm used on machine learning.
What I want to achieve is fake news detection. What I have done is preprocess the articles, and transform them into vectors. I use Glove as the embedding layer in the NN. I've tried to train using the NN model without GA, and it worked fine. Then I applied the NN to PyGAD GA following the tutorial: How To Train Keras Models Using the Genetic Algorithm with PyGAD, the process seemed to be running fine, but the fitness score wasn't going up at all even after 200 generations. I've tried to change the mutation method and some other hyper parameters, but it doesn't seem to change the outcome. What have I done wrong in the process of building the PyGAD GA model? Most of the PyGAD model settings are the same as the examples in the tutorial above.
To specify the problem I am encountering: Below is the main PyGAD code I am using:
Training input(X_train):
...ANSWER
Answered 2021-May-01 at 18:28Your model has large number of parameters (>6.1M). Only the embedding layer has 6M for itself. For a number of parameters like that, the genetic algorithm is expected to take much time for training the model. It does not mean you made a problem. I already tried working with a huge CNN before and there was a progress but very small.
According to the capabilities of your machine, you should increase the number of solutions as much as possible. Also use as many generation as possible.
Thanks for using PyGAD!
QUESTION
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:
...ANSWER
Answered 2021-Feb-19 at 05:39I did not try the project you mentioned. You may try PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms. It is open-source where you can find the code at GitHub.
It is simple to use which allows you to control the crossover, mutation, and parent selection operators in an easy way. You can also control many parameters of the genetic algorithm using PyGAD.
PyGAD also works with a user-defined fitness function so you can adapt it to a wide-range of problems.
After installing PyGAD (pip install pygad), here is a simple example to get started that tries to find the best values for W1, W2, and W3 that satisfies the following equation:
44 = 4xW_1 - 2xW_2 + 1.2xW_3
QUESTION
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:
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?
...ANSWER
Answered 2020-Mar-14 at 22:53Here is a function called crossover that takes two parents and a crossover point. The parents should be lists of integers of the same length. The crossover point is the point before which genes get exchanged, as defined in the article that you linked to. It returns the two offspring of the parents.
QUESTION
ANSWER
Answered 2019-Jul-10 at 10:51
RouletteWheelSelectiondoesn't return parents with 2 individuals
- Assuming the population contains
10individuals - During
RouletteWheelSelectionyou'll be asked to choosenParentsout of the10,5for example
Obviously
nParentsshould not exceedPopulationSizeYou have set
nParents = 16whilePopulationSize = 10
- The correct word will be
nIndividualsinstead ofnParents expectationmeans probability, a value comprised between 0 and 1, also calledrelative fitness
QUESTION
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:
...ANSWER
Answered 2019-Jun-16 at 18:15It's because you're using random bytes to generate IEEE-754 numbers. You should not do that because IEEE-754 defines the structure for these numbers and using random byte input won't give you random numbers because some bits represent things like the is Not-a-Number field, and NaN values are "viral" and invalidate other calculations.
To generate random Double numbers you should use System.Random.NextDouble().
QUESTION
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?
...ANSWER
Answered 2018-Dec-28 at 16:28Its not exactly 10 parents, on average 10 parents. Following is the pseudo code, which I follows.
QUESTION
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
...ANSWER
Answered 2018-Dec-20 at 00:56So, the line you're looking at is the termination condition. The evolution stops when an individual with fitness greater than 100 is found or after 1000 generations. I've done a lot of work with MOEAs, but I'm not too familiar with DEAP. That disclaimer aside, it looks like it's not evolving separate populations, just doing parallelized evaluation. So there's only one population. From the docs, it looks like you could take onemax.py and slot in a multiprocessing pool by doing this:
QUESTION
I came across this notation today as going through this tutorial here:https://towardsdatascience.com/genetic-algorithm-implementation-in-python-5ab67bb124a6
...ANSWER
Answered 2018-Oct-29 at 08:57% is a operator in python, i-e Modulus operator - it gives remainder of the division of left operand by the right
QUESTION
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:
...ANSWER
Answered 2018-Oct-19 at 17:11In this function,
QUESTION
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?
...ANSWER
Answered 2018-Sep-27 at 22:35A very long comment:
Genetic algorithms can be nested. Put your genetic solution finder into a fitness function. Give it to a parent genetic algorithm. Have them search results by "optimizing input vector" from outer GA and "optimizing goal" from inner GA.
You can even add a third layer of GA, to test the construction parameters of middle layer GA because we may not know what kind of search space we need. If we knew it, then we wouldn't need to optimize that vector.
You can even decrease dimensions of problem per GA this way.
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Install Genetic-Algorithm
You can use Genetic-Algorithm like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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