Questions tagged [genetic-algorithms]

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

Filter by
Sorted by
Tagged with
0
votes
0answers
7 views

Difference between ABC and GA for parameter inference?

If I have an agent-based model and I want to infer the parameters, I would normally used ABC, but I was recently working with someone who was using GA for a very similar problem - which method is best ...
0
votes
0answers
8 views

Can the same individual from a population be chosen in a genetic algorithm?

One population contains 10 individuals, from these 10, 8 are picked based on a selection mechanism. Let's say they are picked using the rank based selection mechanism (so the best ranked has a higher ...
1
vote
0answers
23 views

Statistical test suggestion for algorithm comparison

I have 8 different algorithms (based on well-known genetic algorithm). Each algorithm is tested with 50 independent runs for 4 different benchmark problems. Also, each problem has 24 different cases ...
0
votes
0answers
16 views

What statistical tests can determine if multiple population differences reject or accept the null hypothesis?

I have four populations corresponding to fitness of chromosomes in a GA application. One population is using 1-P crossover, another using 2-P, and so on. ...
0
votes
0answers
40 views

Can genetic algorithms be used for statistical inference?

Is it possible to use genetic algorithms for (pseudo-) statistical inference? I.e., something like constructing confidence intervals from stable generations? I hope my question make sense & ...
0
votes
0answers
22 views

Combining a neural network classifier with genetic algorithm to do the task likes what generative adversarial network does?

Recently I read some materials about GANs(Generative adversarial network). I know that GANs uses a discriminator network to distinguish the expected output(let's say that it's an image),and a ...
1
vote
0answers
7 views

Particle Swarm Optimisation Explained

Is anyone able to provide an intuitive explanation of how particle swarm optimisation works? For example how to minimise a function f(x,y,z). Also, does particle swarm optimisation work for multi-...
0
votes
1answer
41 views

Genetic Algorithms for Feature Selection

Is anyone able to provide a simple explanation of how genetic algorithms can be used for feature selection in machine learning?
0
votes
0answers
21 views

What is best way to crossover[Genetic Algorithms]

In Genetic Algorithms there are five phases Initial population Fitness function Selection Crossover Mutation I have solved 2-dimentional problem using this algorithm ...
1
vote
0answers
20 views

Ideas for determining the optimal sequence of calls and emails to maximize the probability of a sales lead converting to a sale?

I have a large data set of sales leads that are in the form of a lead_id, a sequence of binary integers that denote the order of emails and phone calls made to a sales lead, and the binary outcome of ...
1
vote
1answer
33 views

What does summary of the model (T-stat and p-values) after Matching using R's “Matching” package indicate?

I am using the GenMatch and Match functions in R's Matching package to balance covariates in an observational study via Genetic Matching. However, the documentation for this package does not describe ...
0
votes
0answers
51 views

MLRose Specific Min and Max Values for each value Random Optimization

I'm currently working on a project where I am creating a genetic algorithms using the MLRose library (link) to find optimal values for a set of insurance factors. ...
0
votes
0answers
25 views

Efficient algorithm for finding optimal number of breakpoints for piecewise regression

I aim to implement a module in python that does the following: 1) Upon taking training data, fit a piecewise regression with $n$ breakpoints. 2) Determine how well of a fit it is to the data (I ...
1
vote
1answer
23 views

Is Crossover only connected to Genetic algorithm?

Is the idea of crossover only restricted to Genetic Algorithms ? Are there any other evolutionary algorithms that uses crossover(even under another name ) ? If an algorithm uses crossover but does ...
1
vote
0answers
34 views

Best method to investigate treatment effect after creating a Matched control group using Gentic Matching (with replacement)?

Project background: I have data on patients who received varying amounts of therapy dose during treatment of stroke-induced paralysis. I wish to investigate if there are differences in motor-function ...
0
votes
0answers
153 views

Training error higher than test error and validation error

I am training a genetic algorithm for classification and strangely, the training error is consistently HIGHER than the validation and test error. The training and validation set are both small size ...
0
votes
0answers
22 views

Optimation Association Rule Using Genetic Algorithm

i have association rules that i got from FP-Growth algorithm and i want to optimize that association rules using Genetic Algorithm so i can get the strong rules. Does anyone know how to bring the ...
1
vote
0answers
92 views

Advantage and Disadvantage of genetic programming [closed]

Are genetic programming and genetic algorithm the same ? If not how are they different? what are the advantage and the disadvantages of them?
2
votes
0answers
20 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 ...
0
votes
0answers
91 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-...
0
votes
1answer
414 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 ...
0
votes
1answer
95 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 ...
0
votes
0answers
23 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 ...
0
votes
0answers
27 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 ...
1
vote
1answer
33 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 ...
1
vote
0answers
25 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 ...
1
vote
1answer
39 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 (...
1
vote
0answers
23 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 ...
1
vote
1answer
90 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 ...
0
votes
1answer
32 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 ...
2
votes
1answer
67 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 ...
2
votes
0answers
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. ...
1
vote
0answers
61 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 ...
0
votes
2answers
149 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 ...
1
vote
1answer
248 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 ...
0
votes
1answer
42 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 ...
2
votes
0answers
223 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 ...
0
votes
1answer
173 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 ...
1
vote
1answer
400 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 ...
0
votes
1answer
154 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. ...
0
votes
0answers
62 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 ...
0
votes
0answers
121 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 ...
1
vote
1answer
496 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 ...
1
vote
0answers
31 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 ...
3
votes
0answers
214 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. * ...
1
vote
0answers
465 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 ...
0
votes
1answer
32 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 ...
0
votes
0answers
99 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 ...
3
votes
0answers
193 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: ...
1
vote
1answer
397 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 ...