Questions tagged [genetic-algorithms]

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

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Multi-objective optimization problem - fitness

my task is to find a solution for a multiobjective optimization problem, which has two objectives. I'm solving this with a genetic algorithm by using a fitness function, which works fine. My question ...
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How do I create a multi-objective optimisation solution with categories?

I have the following functions: ...
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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