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Questions tagged [genetic-algorithms]

A class of optimization algorithms inspired by (or emulating) biological evolution.

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Testing Evolutionary Algorithm output with Mann-Whitney Wilcoxon Test

I have designed a hybrid Evolutionary Algorithm for my research using Genetic Algorithms. Genetic Algorithms have different operators such as Selection, Crossover and Mutation. These operates are ...
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16 views

What types of AI can learn (evolve) from self play in domains that aren't 'real time' [closed]

I'm not sure if all my terminology is on point so I'll try to explain what I mean in detail, with an example. This is purely a learning exercise for myself! I have a game that I want my AI to learn ...
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29 views

Cross - entropy for two variables with different prob. distributions

Let us say that we have given two random variables with different prob. distributions: A = [0.1, 0, 0.5, ...] B = [0.3, 0.1, 0.03, ...] What should I do when I want to compute the reformulated cross-...
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1answer
19 views

How do I design this multi-objective fitness function for use with genetic algorithm?

I have a multi-objective optimization problem that I am applying genetic algorithm (GA) to solve. Currently, there are only 2 objectives: minimize cost maximize validity The cost minimization is ...
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1answer
28 views

Assignment Problem (Linear Programming, Genetic Algorithm, etc.)

I'm looking for advice on how I should approach a specific problem. Some background first: The problem is about shipments falling into a bin. There are 19 such bins, which are further sorted into 20 ...
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21 views

Training ANN sharing parameters, which algorithm to favor?

Hi all I have a question concerning the algorithm I should use to perform the training of multiple feedforward neural networks with shared parameters. The problem I am trying to solve is a boundary ...
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25 views

Optimization of a noisy function with binary output

What frameworks exist to optimize the following problem: $\theta^\ast = \operatorname*{argmin}\limits_{\theta \in \Theta} f(\theta)$ with the characteristic that $f$ is noisy and has a binary output ...
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1answer
26 views

Score ordinal ranking configuration according to an optimal ranking

I have a list of items, each associated with their optimal ranking in the list. How can I give a score to a given order of the list. For instance, if I know that the optimal ranking is: 4 3 5 1 2 And ...
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24 views

ML for optimizing manufacturing tool programming

I work with some folks who program a rather complex tool that uses a pair of motion systems and a controllable light source to do some material processing. In general it does a pretty good job by ...
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1answer
33 views

Neural network with more than one output

I'd like to make an AI learning how to play 2048 game. I decided to try a genetic algorithm, since I don't have any test examples of correct moves. So my AI should choose one from four possible moves (...
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16 views

How can I select which genetic operators configuration for a permutation problem and why?

I am try to solve a combinatory problem using genetic algorithms and the GA package in R. Essentially my problem statement is the following: Given a fitness function $f(X)$, where $X$ is a division ...
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1answer
66 views

Can I use genetic algorithm without an *explicit* objective function?

I'm confused with the following topic. I have a simulator with 3 real parameters as input and 1 real parameter as output. I don't have a function representing the relation between the parameters and ...
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26 views

Would a genetic algorithm be used for this type of scheduling optimization?

Let me preface this by saying I'm not an experienced data scientist/statistician so I apologize if this is trivial. My problem statement is as follows: There are N events that happen from time A to ...
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1answer
48 views

Genetic algorithm - mutation

I was trying to understand the principle of a GA and implemented it myself for some games and always got to the following question; Usually the evolving of a generations works the following: testing ...
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35 views

Train a neural network for tic tac toe with a genetic algorithm [duplicate]

I was trying to implement a genetic algorithm for the game 'tic tac toe'. How I am doing it at the moment is the following: Initiliaze 50 random networks Let each network play against each network. ...
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35 views

How to modify a Genetic Algorithm to optimise dependent variables?

Say that I want to build a classifier that performs the following: Encode each feature from a dataset (X encodings available) Perform a number of computations on the encoded values (Y number of ...
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2answers
54 views

Performing a uniform crossover of parents

I'm trying to do a crossover of two vectors in my program. There are two vectors, each a parent in a sense, and they're both a series of random binary digits (1's and 0's). My goal is to randomly ...
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1answer
164 views

Exploding gradients and non-gradient-based optimization methods

I was wondering if there is any literature available on training systems, which may show exploding gradients (e.g. recurrent neural networks), by using non-gradient based optimization methods. As far ...
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1answer
40 views

question about training and testing in neural network model coupled with genetic algorithm

I have split my dataset into training and testing. My model is fix which is give same result in every running and give a good accuracy. I want to use my neural network model coupled with genetic ...
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75 views

Evolutionary algorithms for model selection

I've recently come across a few encounters where people are using genetic programming or genetic algorithms to build "best" performing models. gplearn is an example of genetic programming used for ...
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1answer
79 views

R: Genetic Algorithm supporting dynamic constraints

Is there any package available or some other approach to implement constraints like (x1 < x2) or even more complex relationships provided by some function. Another desired option would be a ...
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1answer
162 views

What is evolved faster, ANN+Genetic Algorithms or NEAT?

I'm making a cube learn to jump a jumprope by itself, and I don't know what approach would evolve to the most fitness, the fastest. One approach could be setting up a basic feed-forward ANN with 1 ...
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1answer
102 views

Crossover and mutation with constraints

I had some experience with genetic algorithms during my computer science studies. I wanted to refresh my knowledge and decided to write a simple prototype for automated seating people at tables (e.g. ...
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52 views

How to measure and interpret correlation between non-normal variables [duplicate]

How should I measure correlation between, for example, population size or crossover probability and time ? Pearson coefficient seems like an obvious choice. However my data do not follow normal ...
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76 views

Sample a probability distribution with an evolutionary algorithm?

I've been doing some initial level reading on Markov chain Monte Carlo (MCMC). For what I can tell given a probability distribution $P(x_1, x_2, ..., x_N)$ (dependent on $N$ parameters), MCMC ...
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1answer
289 views

How to test the robustness of a genetic algorithm?

As we known, the genetic algorithm is a random procedure, and its results are related to the initial random seed. From a paper(Lucas. et al., 2015 ), it says "A genetic algorithm can therefore ...
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24 views

Feedforward NN for GA with multiple types of actions (outputs)

I'm working on an AI model that needs to efficiently assign jobs to workers depending on job.requiredResources and worker.totalResources`. When I say efficiently, I ...
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199 views

My difficulties with regard to estimation of parameters using genetic algorithms in R language

* I just came to know that the post was unlocked, meanwhile I have re-posted the question on this forum. But I am posting the edited version here as well as I am unaware of the rules in this regard. * ...
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367 views

Genetic algorithms on Titanic data set - overfitting or the real deal?

As you're likely aware, predicting survivors from the Titanic disaster has been turned into an educational data science challenge on Kaggle.com. A few "competitors" have perfect scores, presumably due ...
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1answer
31 views

Is there a standardized way to incorporate domain knowledge into population based optimization algorithm?

In metaheuristics, domain knowledge can be used to generate initial population to improve convergence rates in population based algorithms. (e.g. a good example is in this paper) Is it possible to ...
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81 views

Approach to optimize parameters to maximize throughput via Genetic Algorithm

I need some ideas to approach the following problem. Problem: I need to set optimum parameters to achieve maximum output dependent on the input type. Input: I have 5 different rocks (say, various ...
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0answers
123 views

Should I use random fixed mutation or random percentual mutation?

I'm evolving neural networks, partly by mutating connection weights and neuron biases. Right now i'm mutating weights as follows: ...
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1answer
320 views

Training an agent to play Flappy Bird

My goal is to train a model to play an endless game such as Flappy Bird. I've seen demo videos where the author explains that they used a neural network and genetic algorithm to train the net. I know ...
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1answer
67 views

Is nested mutation effective?

While running my genetic algorithms I notice that they get stuck in local optima quite a bit. Genomes are often no more than 3 mutations away from getting a really big boost in fitness. However, when ...
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1answer
94 views

Checkers playing Neural Network evolved with Genetic Algorithm becomes too sensitive to input data changes

I recently embarked on a very ambitious project and I have to say it has turned out a lot better than I expected, I succeeded in coding from scratch a neural network that plays checkers at a very ...
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1answer
83 views

Do different species create offspring (NEAT)?

So I'm implementing NEAT in Javascript, however i'm not sure if different species should crossover. So what i'm understanding is that offspring will only be created from parents originating from the ...
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97 views

Is the NEAT algorithm unbalanced?

So I'm developing an implementation of the NEAT algorithm. I understand how it works. But during my testing phase I saw something interesting, related to this quote: In the add node mutation an ...
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46 views

Is it possible to write a GA without giving it a target value? [closed]

For example, if i write "helo wrld" in a text box could i write an algorithm that would change it to "hello world" or find all the closest solutions to what i write? without actually telling the ...
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333 views

Limiting selected variables in Genetic Algorithm Feature Selection

I am trying to find a set of good predictors using carets GA in R to train a few classification models. My dataset consists of around 4500 rows of 96 independent variables. I want to use GA to, ...
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1answer
57 views

Can someone point me towards research works relevant to Importance or Weighting Datapoints like SAW(Stepwise adaptation of weights) technique?

I am working on Fitness case importance for Symbolic Regression and found a Paper "Step-wise Adaptation of Weights for Symbolic Regression with Genetic Programming" which talks about weights of ...
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1answer
1k views

Genetic algorithm maximization of 2 variables

I am trying to teach myself more machine learning concepts. I have found a textbook and am trying to work through the exercises in it. Could anyone help me with this example? Example: Use genetic ...
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1answer
426 views

convolutional neural network vs Genetic algorithms which one has a better object recognition ratio

I have read good things about genetic algorithms and convolutional neural networks when it comes to object recognition. I would like to train a software to recognize any computer that has ever been on ...
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1answer
3k views

Using genetic algorithm for hyperparameter optimization

In machine learning, I've learned one of the ways to optimize hyperparameters of a model is to do a grid search, which tests model for evenly spaced out values of hyperparametrs and determines which ...
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1answer
106 views

Find initial central points of k-means clustering using genetic algorithm

I am implementing genetic algorithm in order to find best initial central points for k-means clustering algorithm. I use this formula for fitness function: $$\sum_{\chi_{j}\in X}{\min_{1\le i\le k}(\...
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5k views

When are genetic algorithms a good choice for optimization?

Genetic algorithms are one form of optimization method. Often stochastic gradient descent and its derivatives are the best choice for function optimization, but genetic algorithms are still sometimes ...
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58 views

Difference or similarity between a biology and genetic algorithm

What id the difference/similarity between a biological chromosome and a chromosome from a genetic algorithm? A biological chromosome represents a specific living organism who can be a result of an ...
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2answers
216 views

Objective function of a Genetic Algorithm

This is my first effort about AI/ML. I have the following problem given by my teacher, Design a simple genetic algorithm in MATLAB, with binary-coded chromosomes, in order to solve pattern ...
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199 views

If a problem can be solved using genetic algorithms, is it an optimization problem?

If I see a problem, I want to know in advance if I can "solve" it with genetic algorithms and I'd like to clarify my understanding of the relationship between genetic algorithms and optimization ...
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15 views

Optimizing individuals by direct comparison (fitness is dependent on other individuals)

I'm participating in an AI challenge and would like to use machine learning or genetic algorithms to optimize my strategy. The strategy is represented by a list of floats (size=7) which are weights ...
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1answer
54 views

Measuring solution quality and allele impact on population fitness for a genetic algorithm

Situation: Let's say you're running a genetic algorithm to improve the way people are interacting with an online service. The alleles in each individual determine the exact behaviour of the service, ...