Questions tagged [genetic-algorithms]

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

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2answers
249 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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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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1answer
43 views

optimization of real values and binary, what is the difference

Many optimization packages provide several ways to find solutions, one of which is the choice between binary search and search for real values. I have a real binary search task, but the search ...
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1answer
337 views

Nonlinear models which are hard to estimate

Genetic algorithms are avoided in econometry literature as often as possible, but still sometimes they are inevitable. The question is: Which well known models are the most difficult to estimate using ...
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Single optimum parameter output for multi-objective optimization (genetic algorithms)

If this question is out of scope for this forum, before closing it please advise me on a better platform to ask my question! I'm very new to this field so apologies if my questions are not clear. I am ...
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Genetic algorithm (GA) for training a generalized linear discriminate classifier

How can genetic algorithms (GA) be used for training a generalized linear discriminate classifier? What would the genes/chromosomes and fitness function be? How can genetic programming (GP) be used ...
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2answers
141 views

Bayesian genetic algorithms?

I'm not sure that "Bayesian genetic algorithms" is a real term or even a concept in research. For a while now, I've preferred genetic algorithms as my go-to heuristic optimization technique, ...
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15 views

Create Fitness score/highscore with multiple variables

I am currently building a Neural network( + genetic algorithms) that learns to fly a 2D drone to a target. My goal is that it achieve all tasks as fast a possible but I want the drone to also fly ...
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Human interpretable Neural Networks?

Say I made Neural Network Trading Algorithm. How to understand what it does? Example How human would do the job. Human look at charts and thinks, and then develops an algorithm like "make ...
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2answers
187 views

Ways to compare feature selection methods

Context: A hyperspectral image is taken (here Indiana Pines) which needs to be reduced to a lower dimension from 200 bands for this GSA is to be used. What will be possible metrics to grade various ...
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1answer
58 views

Sampling from distribution parametrized by $ \log(1-p) $ given $ \log (p) $

The context for the problem is that I'm working with a modified genetic algorithm where the fitness score of each chromosome is given by a log-probability $ \log(p) \in (-\infty, 0) $. Thus, I have a ...
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Question on Machine Learning to Generate Genetic Algorithms with Highest Sensitivity and Specificity Values

I am new to this website, but I am a researcher who has no experience in ML who is trying to generate molecular algorithms that use a combination of genetic markers in order to correctly predict ...
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1answer
140 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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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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1answer
23 views

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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1answer
31 views

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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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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2answers
86 views

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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1answer
170 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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Benefits of using genetic algorithm

Can anyone explain to me the benefits of the genetic algorithm compared to other traditional search and optimization methods?
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1answer
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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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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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1answer
391 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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4answers
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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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8answers
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Train a Neural Network to distinguish between even and odd numbers

Question: is it possible to train a NN to distinguish between odd and even numbers only using as input the numbers themselves? I have the following dataset: ...
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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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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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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
102 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?
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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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1answer
110 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 ...
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1answer
49 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 ...
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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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5answers
39k views

Backpropagation vs Genetic Algorithm for Neural Network training

I've read a few papers discussing pros and cons of each method, some arguing that GA doesn't give any improvement in finding the optimal solution while others show that it is more effective. It seems ...
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137 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
1k 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
4k views

Constrained assignment problem (Linear Programming, Genetic Algorithm, etc...)

I'm looking for advice on how I should approach a specific problem. I have about 1000 shops that I have to assign to about 20 different supply centers out of a possible 28, and I'm trying to pick the ...
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3answers
3k views

Gradient Based Learning Algorithms vs Global Optimization Learning Algorithms for Neural Networks

Neural Networks are usually trained using a gradient based learning algorithm, such as the back propagation algorithm or some variant of it, but can you use global optimization algorithms, such as the ...
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1answer
6k 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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58 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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32 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
42 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
52 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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1answer
170 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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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
204 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}(\...