Questions tagged [genetic-algorithms]

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

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Best Statistical Techniques for Order Sensitive Data

Problem I am writing a Genetic Algorithm (GA) that aims to find the best order of performing a given set of tasks to fulfill an objective. Imagine there are a set of tasks ...
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What are the rules for evolving a partially connected neural network using a genetic algorithm?

I am working on a project where I need to implement the NEAT algorithm in python. after doing some research I came across an issue that I can't seem to find a solution for online, I hope this is the ...
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is there only one correct weight and bias for neural network?

I solved the xor problem using neural network... but I'm really confused my neural network structure looks like this! the first result was like... and the second result was like... could there be ...
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Reweigh a sample obtained through genetic algorithms to emulate rejection sampling

I have a function $f(X) \to \text{true/false}$ where $X$ is a parameter vector of large size (say 100 elements). What I'd like to do is to sample from the posterior of $X|f(X)=\text{true}$. Let's ...
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How to minimise a quantity using a Genetic Algorithm

I'm somewhat new to Genetic Algorithms and I've been exploring the use of them to minimize a quantity (Y), where my optimum value is a small negative value. I've implemented an algorithm that has a ...
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How would you minimize the sum of squares if the predictive function is a black box?

I'm solving an optimization problem, using the mean squared error: $$ \arg\min_{\mathcal{M}} ||y - \hat{y}|| $$ $y$ is the true value and $\hat{y}$ is obtained from some black box function. $\mathcal{...
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Why don't get the expected result despite the high accuracy rate?

I have a database of images of 213 examples (7 classes). First, I extracted the features where I got 212 features. CAD, I maintain a data matrix of 213x212. I used the genetic algorithm for both ...
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106 views

What is the relationship between mean squared error and classification error?

I've trained a network using a genetic algorithm and I have two possible fitness functions for my GA: MSE and CErr. If I use MSE as my fitness function, over time MSE decreases and classification ...
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How do I use matrix math in irregular neural networks generated from neuroevolution (NEAT)?

I understand how to structure the matrix when every node in a layer is fully connected to every node in adjacent layers and I understand that in "irregular" neural networks I can just process each ...
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Alternatives to Genetic Search to find optimla values?

I have a small program that I can configure via a simple input number (integer) called a 'grain size'. I've noticed that the run time can vary based on this number's value. I wrote up my search like ...
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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 ...
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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 ...
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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. ...
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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 & ...
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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-...
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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?
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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 ...
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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 ...
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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 ...
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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 ...
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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?
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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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118 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
665 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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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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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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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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38 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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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
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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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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
120 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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1answer
36 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
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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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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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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
181 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
275 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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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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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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202 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
554 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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163 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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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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126 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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547 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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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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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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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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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 ...