Answers.Body: <p>The island model and niching mentioned by NietzscheanAI are well known ways to isolate populations. However, the populations are not really isolated as individuals migrate from one population to the other. In these cases depending on the sampling strategy used to sample parents for crossover migrating individuals may dominate a population causing rapid convergence.</p>
  
  <p><a href="https://en.wikipedia.org/wiki/Coevolution" rel="nofollow noreferrer">Co-evolution</a> is a truly isolated population approach first introduced by Reed et al. in 1967. Two kinds of co-evolution exist namely co-operative co-evolution and competitive co-evolution. While hybrid models exist co-evolved islands such as <a href="https://www.researchgate.net/publication/275275723_Competitive_Two-Island_Cooperative_Co-evolution_for_Training_Feedforward_Neural_Networks_for_Pattern_Classification_Problems" rel="nofollow noreferrer">here</a> and <a href="https://pdfs.semanticscholar.org/e8c3/2c1244cb04708d5c10c1d4ed3cd30fb2264a.pdf" rel="nofollow noreferrer">here</a>, generally co-evolution is isolated and do not migrate individuals.</p>
  
  <p>Co-operative co-evolution evolves two or more populations that work together to solve a problem, and competitive co-evolution compete where a gain in one population is a loss for another.</p>
  
  <p>Generally the fitness function is altered from explicit to implicit and various techniques are used to do this.</p>
  
  <p>For more information </p>
  
  <ul>
  <li><a href="https://arxiv.org/pdf/1506.05082.pdf" rel="nofollow noreferrer">Review of landmark articles in co-evolution</a> </li>
  <li><a href="https://pdfs.semanticscholar.org/e08e/b39ac69e7342c88cd01238665098c8ca613c.pdf" rel="nofollow noreferrer">Rules of engagement</a> </li>
  <li><a href="https://www.cs.tufts.edu/comp/150GA/handouts/nchb-main.pdf" rel="nofollow noreferrer">Co-evolutionary principles</a> </li>
  <li><a href="http://www.sci.brooklyn.cuny.edu/~sklar/teaching/f05/alife/papers/rosin-96coev.pdf" rel="nofollow noreferrer">New methods for
  competitive co-evolution</a></li>
  </ul>
  
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Answers.Text: The formal definition of coevolution is effectively that 'the fitness of one population is a function of (the fitness of) another', so while they are indeed 'completely isolated' with respect to migration of genetic material, they are also applicable only to problems with some dual (co-operative or adversarial) notion of fitness.
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Answers.Body: <p><a href="http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf" rel="nofollow noreferrer">Neuroevolution of Augmented Topologies (NEAT)</a> and algorithm developed by Ken Stanely, does this by partitioning populations into species. This is done by storing innovation numbers for each gene (node/connection). When a never before used structure is added through mutation the innovation number is incremented, by doing this you can calculate a historical compatibility distance between any two genomes by comparing innovation numbers. This is done because adding a new structure may often times initially hurt a genomes fitness but may actually turn out to be valuable after some optimization. By using this structurally focused speciation, new structures are protected as the elitism is handled in individual species not the whole population. </p>
  
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Answers.Body: <p>There have been extensive studies within <em>evolutionary computation</em> in the area of <a href="http://cs.gmu.edu/%7Eeclab/papers/skolicki05analysis.pdf" rel="nofollow noreferrer"><em>island models</em></a> and <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.30.8270&amp;rep=rep1&amp;type=pdf" rel="nofollow noreferrer"><em>niching</em></a> for doing exactly this.</p>
  <p>The advantages of this approach include greater population diversity (which is particularly useful when the problem is multiobjective) and the potential for concurrent execution of each separate population.</p>
  <p>See also the answers to the question <a href="https://stackoverflow.com/q/13775810/3924118">What is the niching scheme?</a>.</p>
  <p>With specific reference to <em>genetic programming</em>, <a href="https://www.sciencedirect.com/science/article/pii/S0743731506001067" rel="nofollow noreferrer">here</a> is a recent paper that uses a parallel island model.</p>
  
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Body: <p>In a classic example of a genetic algorithm, you would have a population and a certain amount of simulation time to evaluate it and breeding. Then proceed to the next generation.</p>
  
  <p>Is it possible, during the simulation process, to have an isolated and small part of the population and keep it evolving in their own little island for some time while the rest of the population continues to evolve normally? 
  After that, they could be reunited with the rest of the population and the end of the simulation would go through. After that, breed the population and continue. </p>
  
  <p>This is a super important part of natural evolution and probably some know if it actually works with genetic programming?</p>
  
Tags: <genetic-algorithms><evolutionary-algorithms><genetic-programming><island-models>
Title: Is it possible to separately evolve a part of the population?
UserDisplayName: mikerson
ThreadText: # Is it possible to separately evolve a part of the population?
  
  (Asked by: username_0 on 2016-10-06)
  
  In a classic example of a genetic algorithm, you would have a population and a certain amount of simulation time to evaluate it and breeding. Then proceed to the next generation.
  
  Is it possible, during the simulation process, to have an isolated and small part of the population and keep it evolving in their own little island for some time while the rest of the population continues to evolve normally?
  After that, they could be reunited with the rest of the population and the end of the simulation would go through. After that, breed the population and continue.
  
  This is a super important part of natural evolution and probably some know if it actually works with genetic programming?
  
  **Tags:** genetic-algorithms, evolutionary-algorithms, genetic-programming, island-models
  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_2 on 2016-10-06. Score: 5 | Accepted answer
  
  There have been extensive studies within *evolutionary computation* in the area of [*island models*](http://cs.gmu.edu/%7Eeclab/papers/skolicki05analysis.pdf) and [*niching*](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.30.8270&rep=rep1&type=pdf) for doing exactly this.
  
  The advantages of this approach include greater population diversity (which is particularly useful when the problem is multiobjective) and the potential for concurrent execution of each separate population.
  
  See also the answers to the question [What is the niching scheme?](https://stackoverflow.com/q/13775810/3924118).
  
  With specific reference to *genetic programming*, [here](https://www.sciencedirect.com/science/article/pii/S0743731506001067) is a recent paper that uses a parallel island model.
  
  
  ---
  
  ### Answer by username_1 on 2018-12-07. Score: 1
  
  The island model and niching mentioned by username_2 are well known ways to isolate populations. However, the populations are not really isolated as individuals migrate from one population to the other. In these cases depending on the sampling strategy used to sample parents for crossover migrating individuals may dominate a population causing rapid convergence.
  
  [Co-evolution](https://en.wikipedia.org/wiki/Coevolution) is a truly isolated population approach first introduced by Reed et al. in 1967. Two kinds of co-evolution exist namely co-operative co-evolution and competitive co-evolution. While hybrid models exist co-evolved islands such as [here](https://www.researchgate.net/publication/275275723_Competitive_Two-Island_Cooperative_Co-evolution_for_Training_Feedforward_Neural_Networks_for_Pattern_Classification_Problems) and [here](https://pdfs.semanticscholar.org/e8c3/2c1244cb04708d5c10c1d4ed3cd30fb2264a.pdf), generally co-evolution is isolated and do not migrate individuals.
  
  Co-operative co-evolution evolves two or more populations that work together to solve a problem, and competitive co-evolution compete where a gain in one population is a loss for another.
  
  Generally the fitness function is altered from explicit to implicit and various techniques are used to do this.
  
  For more information
  
  * [Review of landmark articles in co-evolution](https://arxiv.org/pdf/1506.05082.pdf)
  * [Rules of engagement](https://pdfs.semanticscholar.org/e08e/b39ac69e7342c88cd01238665098c8ca613c.pdf)
  * [Co-evolutionary principles](https://www.cs.tufts.edu/comp/150GA/handouts/nchb-main.pdf)
  * [New methods for
    competitive co-evolution](http://www.sci.brooklyn.cuny.edu/~sklar/teaching/f05/alife/papers/rosin-96coev.pdf)
  
  > **Comments:**
  > **username_2**: The formal definition of coevolution is effectively that 'the fitness of one population is a function of (the fitness of) another', so while they are indeed 'completely isolated' with respect to migration of genetic material, they are also applicable only to problems with some dual (co-operative or adversarial) notion of fitness.  
  
  ---
  
  ### Answer by username_3 on 2018-12-07. Score: 0
  
  [Neuroevolution of Augmented Topologies (NEAT)](http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf) and algorithm developed by Ken Stanely, does this by partitioning populations into species. This is done by storing innovation numbers for each gene (node/connection). When a never before used structure is added through mutation the innovation number is incremented, by doing this you can calculate a historical compatibility distance between any two genomes by comparing innovation numbers. This is done because adding a new structure may often times initially hurt a genomes fitness but may actually turn out to be valuable after some optimization. By using this structurally focused speciation, new structures are protected as the elitism is handled in individual species not the whole population.
  
  
  ---
  
  