<p>The <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.113.6936&amp;rep=rep1&amp;type=pdf">Lamarckian Genetic Algorithm</a> is used in chemoinformatics to screen for potential new drug compounds that can bind with a particular receptor.</p>

<p>The computational problem is to search through a chemical database for candidates that can orient correctly (wrt the possible orientations of the molecule containing the receptor), and to combine that with a conformational search (i.e., one that considers the possible rotatable torsions of the molecule, which <a href="http://en.wikipedia.org/wiki/Conformational_isomerism#Conformation-dependent_reactions">can strongly affect the reaction</a>).</p>

<p>Previously, it was feasible to perform either an orientation search or a conformation search, but not both.  LGA takes advantage of computer speedup, and combines the global search of a genetic algorithm with a local search.</p>

CC BY-SA 2.5
2010-11-08T16:38:25.290Z
2010-11-08T16:38:25.290Z
Aaron Sterling
<p>I've used GAs to solve scheduling problems in manufacturing and education. The fitness function in the first case was how much of the requested items were manufactured in the given time frame, while in the second case the fitness was based on penalizing schedules with conflicts.</p>

<p>If you're interested in applications, here's a link to <a href="http://citeseerx.ist.psu.edu/search?q=%22genetic+algorithm%22&amp;submit=Search&amp;sort=rel">20K+ papers on citeseerx</a> </p>

CC BY-SA 2.5
2010-09-01T17:03:29.850Z
2010-09-01T17:03:29.850Z
Steven A. Lowe
<p>Nasa created a genetic algorithm for <a href="http://ti.arc.nasa.gov/projects/esg/research/antenna.htm">Antenna Design</a>. </p>

<p>The fitness test is as follows:</p>

<blockquote>
  <p>The fitness function used to evaluate
  antennas is a function of the voltage
  standing wave ratio (VSWR) and gain
  values on the transmit and receive
  frequencies. VSWR is a way to quantify
  reflected-wave interference, and thus
  the amount of impedance mismatch at
  the junction. VSWR is the ratio
  between the highest voltage and the
  lowest voltage in the signal envelope
  along a transmission line.</p>
</blockquote>

CC BY-SA 2.5
2010-08-23T20:13:26.633Z
2010-08-23T20:13:26.633Z
The Rook
<p>These are often used in finance, especially for portfolio optimization problems. There are many papers on this subject, but see for example <a HREF="http://econpapers.repec.org/paper/scescecf1/204.htm">Genetic Algorithms in Portfolio Optimization</a>. </p>

CC BY-SA 2.5
2010-08-24T11:09:29.130Z
2010-08-24T11:09:29.130Z
Shane
<p>I can't resist but point out Roger Alsing's work:</p>

<p><a href="http://rogeralsing.com/2008/12/07/genetic-programming-evolution-of-mona-lisa/">http://rogeralsing.com/2008/12/07/genetic-programming-evolution-of-mona-lisa/</a></p>

<p>Represent the image of Mona Lisa using <em>only</em> 50 semi-transparent triangles.</p>

CC BY-SA 2.5
2010-10-04T18:29:10.197Z
That was a very cool project! And I can't resist pointing out another very cool project http://www.tagxedo.com/, in case y'all don't know about it. ;-)
Steven A. Lowe
CC BY-SA 2.5
2010-09-02T16:08:54.780Z
2010-09-02T16:08:54.780Z
Hardy Leung
<p>Antenna design has already been mentioned, and it is an extremely rich domain.  (It is, very directly, what started my motion from electrical engineering to computer science (in the late 90's) and more specifically to bio-inspired computation and artificial intelligence (in the last five years or so.))</p>

<p>In the same vein, I'll add antenna <em>array</em> optimization, especially for phased array optimization, which is all of the headaches of antenna design, and more.  There are opportunities in the entire field of electromagnetic device design, really:  Antennas, antenna arrays, microwave filters, optical gratings, metamaterial device design, all off the top of my head.  A dated survey is <a href="http://rads.stackoverflow.com/amzn/click/0471295450" rel="nofollow">Electromagnetic Optimization by Genetic Algorithms</a>, and a more recent survey is <a href="http://rads.stackoverflow.com/amzn/click/0471488895" rel="nofollow">Genetic Algorithms in Electromagnetics</a>.  (I should really buy that second one.  </p>

<p>I've seen a lot of good papers on non-electromagnetic circuit design as well:  GAs coming up with competitive op-amp or other integrated circuit designs, GAs "learning" to take advantage of the analog imperfections in FPGAs to implement analog functions like clocks, etc.  Even something as simple as dumb, discrete element filter design can be a target for GAs:  I've seen one that factors in q-factors, tolerances, discrete values and soldering parasitic models to get good, manufacturable filters from the parts you have at hand.</p>

<p>These often involve some novel (to me, anyway) circuit representations to get the genetic operators to fit the paradigm, as well as variable size chromosomes. </p>

CC BY-SA 3.0
2012-09-03T16:26:09.947Z
yes the circuit design example has been shown to rediscover or even outdo patented designs. heres an early paper along these line, much later research. [Design of a High-Gain Operational Amplifier and Other Circuits by Means of Genetic Programming](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.40.1577&rep=rep1&type=pdf) by Koza et al 1997
vzn
CC BY-SA 3.0
2012-09-02T22:28:49.440Z
2012-09-02T22:28:49.440Z
Novak
<p>there is some research into using GAs for wine classification. it accurately classifies the variety of wine and the production place ("origin denomination").[1] this is a subset of use of GAs in Agricultural Systems of which there are many applications. [2]</p>

<p>[1] <a href="http://www.captura.uchile.cl/jspui/bitstream/2250/2139/1/Beltran_NH.pdf" rel="nofollow">Feature selection algorithms using Chilean wine chromatograms as examples</a> by N.H.Beltran et al</p>

<p>[2] <a href="http://www.captura.uchile.cl/jspui/bitstream/2250/2139/1/Beltran_NH.pdf" rel="nofollow">State of Art in Genetic Algorithms for Agricultural Systems</a> by Bolboaca et al</p>

CC BY-SA 3.0
2012-09-02T21:06:43.020Z
2012-09-02T21:06:43.020Z
vzn
<p>recently there was a question about using GAs to evolve wind turbine blade designs using fluid dynamics simulations of physical power generated as the fitness function.[1]</p>

<blockquote>
  <p>This video shows the use of a genetic algorithm to develop VAWT wind turbine blades. One of the resulting blades is quite different and seems to simulate well. The breeding software was written in Perl, the display software Java, and the CFD software was OpenFoam. 
  More than 672 CPU hours went into the making of this video.
  Note: I have since discovered I used the wrong viscosity for air on this experiment, so the results aren't valid for use on the earth. (Maybe Jupiter.)</p>
</blockquote>

<p>[1] <a href="http://www.youtube.com/watch?v=YZUNRmwoijw" rel="nofollow noreferrer">"Evolving wind turbine blades"</a> on youtube by "sjh7132". cited by/from the TCS.se question: <a href="https://cstheory.stackexchange.com/questions/12416/to-what-extent-is-it-possible-to-use-genetic-algorithms-to-make-wind-mill-turbin">To what extent is it possible to use genetic algorithms to make wind mill turbine blades more efficient?</a></p>

CC BY-SA 3.0
2012-09-02T20:05:49.360Z
2012-09-02T20:05:49.360Z
2017-04-13T12:32:28.080Z
vzn
<p>The annual GECCO conference (pretty much the premier venue for evolutionary computation research) has a `Real World Applications' track.</p>

<p>See also <a href="http://rain.ifmo.ru/~buzdalov/ec-2015/08-rwa-1.pdf" rel="nofollow">this</a> recent presentation:</p>

CC BY-SA 3.0
2015-10-17T12:29:24.677Z
2015-10-17T12:29:24.677Z
NietzscheanAI
<p>there are many papers on use of GAs for flight control in the aerospace field. many of these are published or searchable by <a href="http://ieeexplore.ieee.org/" rel="nofollow">IEEE explorer</a>. the fitness function generally measures how well/effectively the algorithm controls the flight.</p>

<p>[1] <a href="http://www.sciencedirect.com/science/article/pii/S1270963804001117" rel="nofollow">Flight control system design and optimisation with a genetic algorithm</a> by Fantinutto et al</p>

<p>[2] Application of genetic algorithms to hypersonic flight control. Austin, Jacobs.</p>

<p>[3] 
Multi-core implementation of F-16 flight surface control system using Genetic Algorithm based adaptive control algorithm, Xiaoru Wang</p>

<p>[4] 
Fuzzy logic control based on genetic algorithm to integrated flight control for hypersonic vehicles. by Wang Jian </p>

CC BY-SA 3.0
2012-09-02T21:38:16.973Z
2012-09-02T21:38:16.973Z
vzn
<p>a remarkable, even extraordinary or paradigm shifting use of GAs, highly cited in later surveys, was pioneered by Koza to solve a video game "problem"— namely Pac Man for a proof of principle, but the concept can likely be applied to possibly almost any video game, and the results are definitely far from trivial or "toy". </p>

<p>that is, he evolved algorithms that implement actual <em>behavior</em> to win at playing the game for extended periods of time. results are on the level of performance of amateur or even <em>advanced</em> human players. a fitness function can be either points scored by the algorithm or length of time played (the later will presumably evolve algorithms that survive without scoring points, such as a classic case of "hunting" spaceships in the game Asteroids). the behavior is implemented with "primitives" (eg sense monsters/act by turning etc) and trees that represent the combinations of primitive strategies.</p>

<p>[1] <a href="http://algoval.essex.ac.uk/rep/games/UKCIDiversePacMan.pdf" rel="nofollow">Evolving Diverse Ms. Pac-Man Playing
Agents Using Genetic Programming</a> by Atif M. Alhejali and Simon M. Lucas</p>

<p>[2] <a href="http://www.itu.dk/~edge/CEC2003-1432.pdf" rel="nofollow">Learning to Play Pac-Man: An Evolutionary, Rule-based Approach</a> by Gallagher and Ryan</p>

<p>[3] <a href="http://www.jair.org/media/2368/live-2368-3623-jair.pdf" rel="nofollow">Learning to Play Using Low-Complexity Rule-Based Policies: Illustrations through Ms. Pac-Man</a> by István Szita
András L~orincz</p>

CC BY-SA 3.0
2012-09-03T02:00:36.460Z
TO be very nit-picky, I would draw a distinction between genetic programming, and genetic algorithms.  Certainly they're closely related, though.
Novak
CC BY-SA 3.0
2012-09-03T02:59:07.170Z
@novak agreed & thx for drawing out the distinction that was blurred here. roughly, [GP](http://en.wikipedia.org/wiki/Genetic_programming) is the use of [GAs](http://en.wikipedia.org/wiki/Genetic_algorithm) to discover algorithms, true? yes the _behavior_ found/constructed is basically equivalent to an _algorithm_.. & of course the technique is probably widely applicable outside of video games although not aware of many examples so far.. its very advanced usage.. (although am reminded of another example of koza's which still uses trees in a clever way! need to post that).
vzn
CC BY-SA 3.0
2012-09-02T20:20:48.167Z
2012-09-02T21:55:27.543Z
2012-09-02T21:55:27.543Z
vzn
<p>The optimizer in relational databases. Examples are <a href="http://www.postgresql.org/docs/8.0/static/geqo-intro2.html">PostgreSQL</a> and <a href="http://h2database.com/html/performance.html?highlight=genetic&amp;search=genetic#firstFound">H2</a>; other databases most likely also use a genetic algorithm. The problem is: selecting the best query plan (the one with the lowest estimated cost) is NP-hard. The fitness test is the estimated cost.</p>

CC BY-SA 2.5
2010-08-24T09:27:01.847Z
2010-08-24T09:27:01.847Z
Thomas Mueller
<p>What are some real world problems that have been solved using a genetic algorithm?   What is the problem?  What is the fitness test used to solve this problem?</p>

CC BY-SA 3.0
2012-09-03T01:27:59.707Z
note consensus that [TCS.se scope adj'd](http://meta.cstheory.stackexchange.com/a/1574/7884) since above comments to be more open to application-of-theory
vzn
CC BY-SA 3.0
2012-09-03T14:38:31.710Z
But this isn't about an application of theory.  It's about an application of practice.
Jeffε
CC BY-SA 3.0
2012-09-03T15:56:01.847Z
?? there is a theory of genetic algorithms and its applied to particular problems. see eg [the koza invention machine, popular science](http://www.popsci.com/scitech/article/2006-04/john-koza-has-built-invention-machine)
vzn
CC BY-SA 3.0
2012-09-05T04:36:08.543Z
the fact that the question is about A and there is also theory about A does not mean there is any theory in this question. the question itself does not ask for the theoretical side of the subject. and the answers accordingly don't provide it. all the answers have the flavor of 'some ppl engineered some heuristics and got some results'.
Sasho Nikolov
CC BY-SA 2.5
2010-08-23T20:15:36.333Z
This should be community wiki (assuming that it's on topic).
Shane
CC BY-SA 2.5
2010-08-23T20:25:16.203Z
I'm not sure genetic algorithms are within scope. We should discuss it here: http://meta.cstheory.stackexchange.com/questions/73/which-metaheuristic-methods-are-admissible-for-discussion
Suresh Venkat
CC BY-SA 2.5
2010-08-23T22:33:50.923Z
This is out of topic.
Marcos Villagra
CC BY-SA 2.5
2010-08-23T23:30:43.227Z
@Marcos Villagra yes there are no more topic left! Because we ran out...
The Rook
CC BY-SA 2.5
2010-09-01T17:27:45.410Z
An intereresting question might have been: are there any scenarios that admit provable guarantees for GAs.
Suresh Venkat
<ds.algorithms><machine-learning><ai.artificial-intel><application-of-theory><ne.neural-evol>
What are some real world applications for genetic algorithms?
The Rook
# What are some real world applications for genetic algorithms?

(Asked by: username_0 on 2010-08-23)

What are some real world problems that have been solved using a genetic algorithm? What is the problem? What is the fitness test used to solve this problem?

**Tags:** ds.algorithms, machine-learning, ai.artificial-intel, application-of-theory, ne.neural-evol

> **Comments:**
> **username_4**: This should be community wiki (assuming that it's on topic).  
> **username_5**: I'm not sure genetic algorithms are within scope. We should discuss it here: http://meta.cstheory.stackexchange.com/questions/73/which-metaheuristic-methods-are-admissible-for-discussion  
> **username_6**: This is out of topic.  
> **username_0**: @username_6 yes there are no more topic left! Because we ran out...  
> **username_5**: An intereresting question might have been: are there any scenarios that admit provable guarantees for GAs.  
> **username_1**: note consensus that [TCS.se scope adj'd](http://meta.cstheory.stackexchange.com/a/1574/7884) since above comments to be more open to application-of-theory  
> **username_2**: But this isn't about an application of theory.  It's about an application of practice.  
> **username_1**: ?? there is a theory of genetic algorithms and its applied to particular problems. see eg [the koza invention machine, popular science](http://www.popsci.com/scitech/article/2006-04/john-koza-has-built-invention-machine)  
> **username_3**: the fact that the question is about A and there is also theory about A does not mean there is any theory in this question. the question itself does not ask for the theoretical side of the subject. and the answers accordingly don't provide it. all the answers have the flavor of 'some ppl engineered some heuristics and got some results'.  

---

## 12 answers

### Answer by username_12 on 2010-08-24. Score: 10 | Accepted answer

The optimizer in relational databases. Examples are [PostgreSQL](http://www.postgresql.org/docs/8.0/static/geqo-intro2.html) and [H2](http://h2database.com/html/performance.html?highlight=genetic&search=genetic#firstFound); other databases most likely also use a genetic algorithm. The problem is: selecting the best query plan (the one with the lowest estimated cost) is NP-hard. The fitness test is the estimated cost.


---

### Answer by username_7 on 2010-11-08. Score: 8

The [Lamarckian Genetic Algorithm](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.113.6936&rep=rep1&type=pdf) is used in chemoinformatics to screen for potential new drug compounds that can bind with a particular receptor.

The computational problem is to search through a chemical database for candidates that can orient correctly (wrt the possible orientations of the molecule containing the receptor), and to combine that with a conformational search (i.e., one that considers the possible rotatable torsions of the molecule, which [can strongly affect the reaction](http://en.wikipedia.org/wiki/Conformational_isomerism#Conformation-dependent_reactions)).

Previously, it was feasible to perform either an orientation search or a conformation search, but not both. LGA takes advantage of computer speedup, and combines the global search of a genetic algorithm with a local search.


---

### Answer by username_8 on 2010-09-01. Score: 6

I've used GAs to solve scheduling problems in manufacturing and education. The fitness function in the first case was how much of the requested items were manufactured in the given time frame, while in the second case the fitness was based on penalizing schedules with conflicts.

If you're interested in applications, here's a link to [20K+ papers on citeseerx](http://citeseerx.ist.psu.edu/search?q=%22genetic+algorithm%22&submit=Search&sort=rel)


---

### Answer by username_0 on 2010-08-23. Score: 6

Nasa created a genetic algorithm for [Antenna Design](http://ti.arc.nasa.gov/projects/esg/research/antenna.htm).

The fitness test is as follows:

> The fitness function used to evaluate
> antennas is a function of the voltage
> standing wave ratio (VSWR) and gain
> values on the transmit and receive
> frequencies. VSWR is a way to quantify
> reflected-wave interference, and thus
> the amount of impedance mismatch at
> the junction. VSWR is the ratio
> between the highest voltage and the
> lowest voltage in the signal envelope
> along a transmission line.


---

### Answer by username_4 on 2010-08-24. Score: 6

These are often used in finance, especially for portfolio optimization problems. There are many papers on this subject, but see for example [Genetic Algorithms in Portfolio Optimization](http://econpapers.repec.org/paper/scescecf1/204.htm).


---

### Answer by username_9 on 2010-09-02. Score: 6

I can't resist but point out Roger Alsing's work:

<http://rogeralsing.com/2008/12/07/genetic-programming-evolution-of-mona-lisa/>

Represent the image of Mona Lisa using *only* 50 semi-transparent triangles.

> **Comments:**
> **username_8**: That was a very cool project! And I can't resist pointing out another very cool project http://www.tagxedo.com/, in case y'all don't know about it. ;-)  

---

### Answer by username_10 on 2012-09-02. Score: 4

Antenna design has already been mentioned, and it is an extremely rich domain. (It is, very directly, what started my motion from electrical engineering to computer science (in the late 90's) and more specifically to bio-inspired computation and artificial intelligence (in the last five years or so.))

In the same vein, I'll add antenna *array* optimization, especially for phased array optimization, which is all of the headaches of antenna design, and more. There are opportunities in the entire field of electromagnetic device design, really: Antennas, antenna arrays, microwave filters, optical gratings, metamaterial device design, all off the top of my head. A dated survey is [Electromagnetic Optimization by Genetic Algorithms](http://rads.stackoverflow.com/amzn/click/0471295450), and a more recent survey is [Genetic Algorithms in Electromagnetics](http://rads.stackoverflow.com/amzn/click/0471488895). (I should really buy that second one.

I've seen a lot of good papers on non-electromagnetic circuit design as well: GAs coming up with competitive op-amp or other integrated circuit designs, GAs "learning" to take advantage of the analog imperfections in FPGAs to implement analog functions like clocks, etc. Even something as simple as dumb, discrete element filter design can be a target for GAs: I've seen one that factors in q-factors, tolerances, discrete values and soldering parasitic models to get good, manufacturable filters from the parts you have at hand.

These often involve some novel (to me, anyway) circuit representations to get the genetic operators to fit the paradigm, as well as variable size chromosomes.

> **Comments:**
> **username_1**: yes the circuit design example has been shown to rediscover or even outdo patented designs. heres an early paper along these line, much later research. [Design of a High-Gain Operational Amplifier and Other Circuits by Means of Genetic Programming](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.40.1577&rep=rep1&type=pdf) by Koza et al 1997  

---

### Answer by username_1 on 2012-09-02. Score: 3

there is some research into using GAs for wine classification. it accurately classifies the variety of wine and the production place ("origin denomination").[1] this is a subset of use of GAs in Agricultural Systems of which there are many applications. [2]

[1] [Feature selection algorithms using Chilean wine chromatograms as examples](http://www.captura.uchile.cl/jspui/bitstream/2250/2139/1/Beltran_NH.pdf) by N.H.Beltran et al

[2] [State of Art in Genetic Algorithms for Agricultural Systems](http://www.captura.uchile.cl/jspui/bitstream/2250/2139/1/Beltran_NH.pdf) by Bolboaca et al


---

### Answer by username_1 on 2012-09-02. Score: 3

recently there was a question about using GAs to evolve wind turbine blade designs using fluid dynamics simulations of physical power generated as the fitness function.[1]

> This video shows the use of a genetic algorithm to develop VAWT wind turbine blades. One of the resulting blades is quite different and seems to simulate well. The breeding software was written in Perl, the display software Java, and the CFD software was OpenFoam.
> More than 672 CPU hours went into the making of this video.
> Note: I have since discovered I used the wrong viscosity for air on this experiment, so the results aren't valid for use on the earth. (Maybe Jupiter.)

[1] ["Evolving wind turbine blades"](http://www.youtube.com/watch?v=YZUNRmwoijw) on youtube by "sjh7132". cited by/from the TCS.se question: [To what extent is it possible to use genetic algorithms to make wind mill turbine blades more efficient?](https://cstheory.stackexchange.com/questions/12416/to-what-extent-is-it-possible-to-use-genetic-algorithms-to-make-wind-mill-turbin)


---

### Answer by username_11 on 2015-10-17. Score: 1

The annual GECCO conference (pretty much the premier venue for evolutionary computation research) has a `Real World Applications' track.

See also [this](http://rain.ifmo.ru/~buzdalov/ec-2015/08-rwa-1.pdf) recent presentation:


---

### Answer by username_1 on 2012-09-02. Score: 1

there are many papers on use of GAs for flight control in the aerospace field. many of these are published or searchable by [IEEE explorer](http://ieeexplore.ieee.org/). the fitness function generally measures how well/effectively the algorithm controls the flight.

[1] [Flight control system design and optimisation with a genetic algorithm](http://www.sciencedirect.com/science/article/pii/S1270963804001117) by Fantinutto et al

[2] Application of genetic algorithms to hypersonic flight control. Austin, Jacobs.

[3]
Multi-core implementation of F-16 flight surface control system using Genetic Algorithm based adaptive control algorithm, Xiaoru Wang

[4]
Fuzzy logic control based on genetic algorithm to integrated flight control for hypersonic vehicles. by Wang Jian


---

### Answer by username_1 on 2012-09-02. Score: 1

a remarkable, even extraordinary or paradigm shifting use of GAs, highly cited in later surveys, was pioneered by Koza to solve a video game "problem"— namely Pac Man for a proof of principle, but the concept can likely be applied to possibly almost any video game, and the results are definitely far from trivial or "toy".

that is, he evolved algorithms that implement actual *behavior* to win at playing the game for extended periods of time. results are on the level of performance of amateur or even *advanced* human players. a fitness function can be either points scored by the algorithm or length of time played (the later will presumably evolve algorithms that survive without scoring points, such as a classic case of "hunting" spaceships in the game Asteroids). the behavior is implemented with "primitives" (eg sense monsters/act by turning etc) and trees that represent the combinations of primitive strategies.

[1] [Evolving Diverse Ms. Pac-Man Playing
Agents Using Genetic Programming](http://algoval.essex.ac.uk/rep/games/UKCIDiversePacMan.pdf) by Atif M. Alhejali and Simon M. Lucas

[2] [Learning to Play Pac-Man: An Evolutionary, Rule-based Approach](http://www.itu.dk/~edge/CEC2003-1432.pdf) by Gallagher and Ryan

[3] [Learning to Play Using Low-Complexity Rule-Based Policies: Illustrations through Ms. Pac-Man](http://www.jair.org/media/2368/live-2368-3623-jair.pdf) by István Szita
András L~orincz

> **Comments:**
> **username_10**: TO be very nit-picky, I would draw a distinction between genetic programming, and genetic algorithms.  Certainly they're closely related, though.  
> **username_1**: @novak agreed & thx for drawing out the distinction that was blurred here. roughly, [GP](http://en.wikipedia.org/wiki/Genetic_programming) is the use of [GAs](http://en.wikipedia.org/wiki/Genetic_algorithm) to discover algorithms, true? yes the _behavior_ found/constructed is basically equivalent to an _algorithm_.. & of course the technique is probably widely applicable outside of video games although not aware of many examples so far.. its very advanced usage.. (although am reminded of another example of koza's which still uses trees in a clever way! need to post that).  

---

