<p>There are some works on this issue. Since you don't specify for what problems you want guidelines I will list some major and some last papers of general interest. They are:</p>

<ul>
<li>C. Witt (2008) <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.67.6556&amp;rep=rep1&amp;type=pdf">Population Size versus Runtime of a Simple Evolutionary Algorithm</a> // Theoretical Computer Science, 403(1), pp. 104-120.</li>
<li>T. Chen, K. Tang, G. Chen and X. Yao <a href="http://dx.doi.org/10.1016/j.tcs.2011.02.016">A large population size can be unhelpful in evolutionary algorithms</a> // Theoretical Computer Science. Article in Press</li>
<li>O. Giel, P.K. Lehre (2010) <a href="http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=5C6F309BC6D8F879F25E041A1E85201B?doi=10.1.1.67.9551&amp;rep=rep1&amp;type=pdf">On the effect of populations in evolutionary multi-objective optimisation</a> // Evolutionary Computation 18 (3), pp. 335-356.</li>
<li>P.S. Oliveto, J. He, X. Yao (2008) <a href="http://citeseer.ist.psu.edu/viewdoc/download;jsessionid=E885D7C12D3ADC5F7AD4F7485AD89CFD?doi=10.1.1.160.131&amp;rep=rep1&amp;type=pdf">Analysis of Population-based Evolutionary Algorithms for the Vertex Cover Problem</a>. In Proceedings of the IEEE World Congress on Computational Intelligence.</li>
<li>P.S. Oliveto, J. He, X. Yao (2007) <a href="http://www.cs.bham.ac.uk/~pkl/teaching/2009/ec/articles/oliveto2007review.pdf">Time Complexity of Evolutionary Algorithms for Combinatorial Optimization: A Decade of Results</a> // International Journal of Automation and Computing. See here 4.1 section for results on population-based EAs.</li>
</ul>

CC BY-SA 2.5
2011-02-26T12:42:26.563Z
2011-02-26T14:14:07.470Z
2011-02-26T14:14:07.470Z
Oleksandr Bondarenko
<p>Genetic algorithms evolve in fewer generations with a larger population, but also take longer to compute a generation. Are there some guide lines for balancing those two factors, in order to arrive at a viable solution as soon as possible?</p>

<p>Also, is this the best place for the question?</p>

CC BY-SA 2.5
2011-02-26T06:45:20.023Z
The question probably belongs on [Cross Validated](http://stats.stackexchange.com/).
chrisaycock
CC BY-SA 2.5
2011-02-26T07:56:54.543Z
I think this question is fine here, and I would like to see it answered here. As far as I understand genetic algorithms do not have as rigorous analysis as many of the common topics on cstheory, but this question is still a cstheory question and I would argue at the research level. We have answered genetic algorithm questions before: http://cstheory.stackexchange.com/questions/844/provable-statements-about-genetic-algorithms There's also a meta discussion on these sort of questions: http://meta.cstheory.stackexchange.com/questions/73/which-metaheuristic-methods-are-admissible-for-discussion
Artem Kaznatcheev
<ds.algorithms><machine-learning><genetic-algorithms>
What is the tradeoff between population size and the number of generations in genetic algorithms
Matt Munson
# What is the tradeoff between population size and the number of generations in genetic algorithms

(Asked by: username_0 on 2011-02-26)

Genetic algorithms evolve in fewer generations with a larger population, but also take longer to compute a generation. Are there some guide lines for balancing those two factors, in order to arrive at a viable solution as soon as possible?

Also, is this the best place for the question?

**Tags:** ds.algorithms, machine-learning, genetic-algorithms
> **Comments:**
> **username_1**: The question probably belongs on [Cross Validated](http://stats.stackexchange.com/).  
> **username_2**: I think this question is fine here, and I would like to see it answered here. As far as I understand genetic algorithms do not have as rigorous analysis as many of the common topics on cstheory, but this question is still a cstheory question and I would argue at the research level. We have answered genetic algorithm questions before: http://cstheory.stackexchange.com/questions/844/provable-statements-about-genetic-algorithms There's also a meta discussion on these sort of questions: http://meta.cstheory.stackexchange.com/questions/73/which-metaheuristic-methods-are-admissible-for-discussion  

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## 1 answers

### Answer by username_3 on 2011-02-26. Score: 15 | Accepted answer

There are some works on this issue. Since you don't specify for what problems you want guidelines I will list some major and some last papers of general interest. They are:

* C. Witt (2008) [Population Size versus Runtime of a Simple Evolutionary Algorithm](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.67.6556&rep=rep1&type=pdf) // Theoretical Computer Science, 403(1), pp. 104-120.
* T. Chen, K. Tang, G. Chen and X. Yao [A large population size can be unhelpful in evolutionary algorithms](http://dx.doi.org/10.1016/j.tcs.2011.02.016) // Theoretical Computer Science. Article in Press
* O. Giel, P.K. Lehre (2010) [On the effect of populations in evolutionary multi-objective optimisation](http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=5C6F309BC6D8F879F25E041A1E85201B?doi=10.1.1.67.9551&rep=rep1&type=pdf) // Evolutionary Computation 18 (3), pp. 335-356.
* P.S. Oliveto, J. He, X. Yao (2008) [Analysis of Population-based Evolutionary Algorithms for the Vertex Cover Problem](http://citeseer.ist.psu.edu/viewdoc/download;jsessionid=E885D7C12D3ADC5F7AD4F7485AD89CFD?doi=10.1.1.160.131&rep=rep1&type=pdf). In Proceedings of the IEEE World Congress on Computational Intelligence.
* P.S. Oliveto, J. He, X. Yao (2007) [Time Complexity of Evolutionary Algorithms for Combinatorial Optimization: A Decade of Results](http://www.cs.bham.ac.uk/~pkl/teaching/2009/ec/articles/oliveto2007review.pdf) // International Journal of Automation and Computing. See here 4.1 section for results on population-based EAs.


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