# Questions tagged [genetic-algorithms]

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

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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 ...
11k views

### 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 ...
1k views

### How to choose between learning algorithms

I need to implement a program that will classify records into 2 categories (true/false) based on some training data, and I was wondering at which algorithm/methodology I should be looking at. There ...
11k views

### 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: ...
12k views

### What language to use for genetic programming

As part of an assignment I'll have to write a genetic programming algorithm that does prediction of atmospheric pollutant levels. Since I have no experience, can anyone point me pointers to ...
27k views

### Benefits of using genetic algorithm

Can anyone explain to me the benefits of the genetic algorithm compared to other traditional search and optimization methods?
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 ...
930 views

### Comparing two genetic algorithms

I have two implementations of a genetic algorithm which are supposed to behave equivalently. However due to technical restrictions which cannot be resolved their output is not exactly the same, given ...
8k views

### How to perform genetic-algorithm variable selection in R for SVM input variables?

I'm using the kernlab package in R to build an SVM for classifying some data. The SVM is working nicely in that it provides 'predictions' of a decent accuracy, however my list of input variables is ...
2k views

### How to check if modified genetic algorithm is significantly better than the original?

My question deals with how to be able to assert that an "improved" evolutionary algorithm is indeed improved (at least from a statistic point of view) and not just random luck (a concern given the ...
1k views

### On what tasks does neuroevolution outperform basic application of neural networks or genetic algorithms?

There has been a recent interest in combining genetic algorithms and neural networks into a general neuroevolution framework. The basic idea, is that your genetic algorithm is evolving the parameters ...
10k views

### How to statistically compare two algorithms across three datasets in feature selection and classification?

Problem background: As part of my research, I have written two algorithms that can select a set of features from a data set (gene expression data from cancer patients). These features are then tested ...
965 views

### Feature construction in R

I am wondering if there are any algorithms (perhaps genetic algorithms) in R for feature construction (deriving candidate predictors from existing predictors)? I am thinking of a routine to test ...
3k views

### Convergence of a genetic algorithm

Does anyone know of any method for deciding when a genetic algorithm is done? In MCMC (e.g, BUGS), several chains are started at different, random points. When they all look the same, it is done. Has ...
3k views

### How to avoid overfitting when using crossvalidation within Genetic Algorithms

This is a long set-up, but the pure intellectual challenge will make it worthwhile I promise ;-) I have marketing data where there is a treatment and a control (i.e a customer gets no treatment). The ...
4k views

### Is there an R optimization package that can handle integer constraints and non-linear objective functions?

I am looking for an optimization routine that can optimize a non-linear objective function with integer constraints. NuOPT for S-Plus, CPLEX, or Matlab include powerful optimization packages for these ...
3k views

### Overfitting in Genetic Programming

I've recently started experimenting with Genetic Programming as an optimization tool. I'm still a little confused as to how to reduce overfitting in this framework. A couple of techniques I've read ...
543 views

### Expected value of item in a sorted list of integers

If you were to take random positive integers, put them into a list, and sort them is there anyway to find the expected value of the kth item in the list? The list is sorted in ascending order. By ...
336 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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### How to decide whether to reuse old code or reinvent the wheel?

For a long time now, I have been thinking about working with neural networks and genetic algorithms. I have never been able to decide whether it makes sense to start writing my own code, or to reuse ...
462 views

### Framework for Symbolic Classification

Please advise good framework for symbolic classification. I am currently using GPTIPS for that but I belive there are better options. What I am trying to do is following: for set of features of my ...
359 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 to compare different heuristics over a large number of test instances

I have 3 different heuristics (H1, H2, H3) that are variation of genetic algorithms combined with some other techniques. The problem that I try to solve is The problem to minimize energy costs for ...
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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### Centroid of nearest-neighbours on a hypersphere as a method for applying crossover in genetic algorithms

I am currently building a genetic algorithm to tune n parameters where n will probably be in the range of ...
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### Using genetic algorithm to tune learning machines

I'm playing with tuning learning machines (specifically a random forest and a support vector machine) using genetic algorithms in R. The only real complication that I've encountered is developing a ...
988 views

### Forecasting Using Genetic Algorithms

I have come across some papers discussing the use of genetic algorithms as a forecasting tool. I don't however understand how a genetic algorithm, which to my (limited) knowledge is used to solve ...
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### Solving a scrambled image puzzle with a genetic algorithm

I want to solve a puzzle such as this one: by using a genetic algorithm. When the number of pieces grow, and maybe some are rotated, the number of combinations become overwhelming. I am hoping that a ...
113 views

### Checkers playing Neural Network evolved with Genetic Algorithm becomes too sensitive to input data changes

I recently embarked on a very ambitious project and I have to say it has turned out a lot better than I expected, I succeeded in coding from scratch a neural network that plays checkers at a very ...
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### Which statistical test should I use to test results from a genetic algorithm vs. an exact method?

I have a known value from some exact algorithm and I have 200 values which were obtained with 200 runs of a genetic algorithm. Now I want to test if on average the results from the GA come from the ...
287 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 ...
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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. * ...
214 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: ...
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### Efficiency of crossover in genetic algorithms

This question is basically clone of . I am reposting it here because in my opinion it is more appropriate site and because I didn't actually see answer itself. Quote from there: I understand the ...
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### How to deal with chromosomes of different lengths genes in genetic algorithm?

If I have 6 products ($x$) and 11 manufacturers ($y$) some products manufactured by some of these manufacturers; ($x,y$) may equal none or any other value. For each combination of $x$ and $y$ there ...
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### Genetic algorithms, genetic programming or machine learning algorithms for solving this problem

I have a problem that consists of finding the optimal solution based on the following criteria: Logic for identifying that event A has occurred (i.e. "find" logic that most accurately categorises an ...
84 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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### 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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### 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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### 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 ...