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Questions tagged [genetic-algorithms]

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

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What are downsides to "genetic matching," particularly outside of causal inference settings?

Multivariate matching methods typically involve two steps. First the user computes $D$, a matrix of the multivariate distances between units. Second, the user applies a matching function (e.g., 1:1 ...
socialscientist's user avatar
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Can you penalize based on difference between training and testing error?

Is it a valid or useful technique to penalize the model based on the objective function + abs(training error - testing error). The error would probably have to be scaled. This seems useful, and I am ...
BigMistake's user avatar
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Mutation probability for simple and complex problems

Is It always better increase the mutation probability if the problem is more complex? I'm doing different experiments trying to check if a higher mutation probability helps to find a solution when the ...
Guillermo Álvarez's user avatar
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Can reparameterizing the parameters for DEoptim improve convergence: whether/how to do it? [closed]

DEoptim is described as DEoptim implements the Differential Evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector as described in Mullen et al. (2011)...
Dan's user avatar
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Genetic Algorithm as engine for Variational Inference?

I'm curious if anyone has used, heard of, or otherwise considered using Genetic Algorithms as an engine for Variational Inference (VI)? My understanding of VI is that it's an optimization algorithm, ...
jbuddy_13's user avatar
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Tic tac toe AI, general questions

Tl;dr - Very new to AIs and neural networks, trying to make tic tac toe. Trying to use a genetic algorithm that plays against other AIs, and the looser leaves. They seem to never learn even to not ...
Osmar's user avatar
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How pairs of actual parents are formed from the mating pool in NSGA-II?

I understand the general idea of how NSGA-II works, however even by reading the original article from Deb and al. (https://ieeexplore.ieee.org/document/996017), flow charts, and so on, I don't ...
Marc's user avatar
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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 ...
auldo's user avatar
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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 ...
mr.T's user avatar
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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 ...
ISquared's user avatar
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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 ...
Alex Craft's user avatar
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2 answers
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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 ...
Girish Srivatsa's user avatar
3 votes
2 answers
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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, ...
jbuddy_13's user avatar
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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 ...
Leonard Tang's user avatar
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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 ...
martinomburajr's user avatar
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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 ...
Tomergt45's user avatar
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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 ...
GazelGoes's user avatar
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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 ...
AlphaBetaGamma96's user avatar
2 votes
2 answers
243 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{...
roulette01's user avatar
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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 ...
Adel Madrid's user avatar
3 votes
1 answer
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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 ...
gator's user avatar
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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 ...
Austin Capobianco's user avatar
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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 ...
Tibo Geysen's user avatar
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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 ...
WhoCares's user avatar
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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 & ...
Adam B.'s user avatar
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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-...
Jimmy Jumper 's user avatar
2 votes
1 answer
286 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?
Mike Tauber's user avatar
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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 ...
statsquestions's user avatar
2 votes
1 answer
260 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 ...
stats_nerd's user avatar
1 vote
1 answer
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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 ...
Paulo's user avatar
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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 ...
stats_nerd's user avatar
1 vote
0 answers
502 views

genetic algorithm vs. genetic programming [closed]

Are genetic programming and genetic algorithms the same? If not, how are they different?
akhil krishnan's user avatar
2 votes
0 answers
32 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 ...
FraserOfSmeg's user avatar
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184 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-...
Łukasz Tulczyjew's user avatar
3 votes
1 answer
3k 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 ...
Jane Wayne's user avatar
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1 answer
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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 ...
Siddharth Mehta's user avatar
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0 answers
79 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 ...
Sorade's user avatar
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0 answers
53 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 ...
jakob-r's user avatar
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1 vote
1 answer
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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 ...
simonta55's user avatar
1 vote
0 answers
27 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 ...
Hucker's user avatar
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1 vote
1 answer
101 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 (...
Maras's user avatar
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1 vote
0 answers
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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 ...
mickkk's user avatar
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1 vote
1 answer
426 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 ...
ignaciovi's user avatar
0 votes
1 answer
62 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 ...
madsthaks's user avatar
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2 votes
1 answer
124 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 ...
Finn Eggers's user avatar
3 votes
0 answers
49 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. ...
Finn Eggers's user avatar
1 vote
0 answers
144 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 ...
Nenovrak's user avatar
0 votes
2 answers
408 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 ...
yqz09's user avatar
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2 votes
1 answer
457 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 ...
LiKao's user avatar
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