Questions tagged [genetic-algorithms]

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

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36
votes
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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4answers
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 ...
22
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2answers
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 ...
18
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8answers
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: ...
15
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2answers
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 ...
14
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5answers
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?
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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 ...
10
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2answers
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 ...
9
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2answers
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 ...
8
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5answers
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 ...
8
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1answer
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 ...
8
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2answers
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 ...
8
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3answers
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 ...
7
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4answers
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 ...
7
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3answers
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 ...
7
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2answers
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 ...
6
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1answer
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 ...
5
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1answer
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 ...
5
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1answer
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 ...
5
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1answer
322 views

Can Metropolis be considered as evolutionary algorithm?

If we compare simple 1+1 evolutionary algorithm (e.g. Droste, Jansen, and Wegener, 2002) 1+1 evolutionary algorithm Set $p_m := 1/n$. Choose randomly an initial bit string $x \in \{0,1\}^...
4
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3answers
1k views

Genetic algorithm for parameter estimation

I am a bit confused about parameter estimation using evolutionary methods and their ability to do such a job. Since I am not that pro in stat I am describing my problem with giving an example. Given ...
4
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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 ...
4
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2answers
439 views

Random number generation

Generating n random variables whose summation will be 1. [I got the answer.] EDIT On genetic algorithm, we have to maintain population. Say, I have two individuals a and b. Every individual consists ...
4
votes
1answer
192 views

Are there any recommended approaches for analysing data from genetic algorithms?

After running a study based on interactive genetic algorithms, I have a univariate data file containing multiple participants each doing multiple generations (blocks) of multiple trials. Is there an ...
4
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1answer
120 views

Rating how closely one graph models another

I'm using a genetic algorithm to generate a string that produces certain results I map into a line/bar graph. I'm trying to rate how closely the results produced by the genetic string compares to a ...
4
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1answer
1k views

Genetic algorithm with constraint in R

I am trying to estimate a set of parameters using a genetic algorithm in R with the 'GA' package. So far I am doing something very simple (which works): ...
4
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1answer
974 views

Seeking a free symbolic regression software [closed]

Now that Formulize / Eureqa started charging $2500 a year for using it and having crippled the trial version, does anyone know of any replacements that can do similar things like find an equation ...
3
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3answers
976 views

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 ...
3
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1answer
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 ...
3
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1answer
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 ...
3
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1answer
524 views

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 ...
3
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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 ...
3
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1answer
284 views

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 ...
3
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1answer
806 views

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 ...
3
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1answer
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 ...
3
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1answer
2k views

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 ...
3
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1answer
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 ...
3
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1answer
93 views

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 ...
3
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0answers
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 ...
3
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0answers
223 views

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. * ...
3
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0answers
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: ...
3
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0answers
66 views

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 ...
3
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0answers
797 views

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 ...
3
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0answers
550 views

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 ...
2
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2answers
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{...
2
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1answer
100 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 ...
2
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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 ...
2
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1answer
80 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 ...
2
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1answer
82 views

Measuring solution quality and allele impact on population fitness for a genetic algorithm

Situation: Let's say you're running a genetic algorithm to improve the way people are interacting with an online service. The alleles in each individual determine the exact behaviour of the service, ...
2
votes
1answer
469 views

Uncertainty in Genetic Algorithm output

I'm applying a simple genetic algorithm to an optimization problem (I need to find a 4-parameter function's global minimum) I start the generations run with say 100...