Answers.Body: <p>In principle, yes, you can also evolve the genetic algorithm (or, in general, evolutionary algorithm), i.e. you can evolve its operations (such as the mutation and cross-over) and hyper-parameters (such as the size of the population or mutation rate). For example, you could use <a href="http://www.shahed.ac.ir/stabaii/Files/CompIntelligenceBook.pdf" rel="nofollow noreferrer">genetic programming</a> to evolve the cross-over operation of a genetic algorithm. However, these genetic operators and hyper-parameters are usually designed and determined by a human and do not change during the evolution process. Nevertheless, there is a framework for evolutionary computation in which reproduction and mutation can also evolve, known as <a href="http://faculty.hampshire.edu/lspector/pubs/ace.pdf" rel="nofollow noreferrer">auto-constructive evolution</a>, and there are other examples in the literature of meta-optimization methods applied to evolutionary algorithms, such as</p>
  <ul>
  <li><p><a href="http://people.idsia.ch/%7Ejuergen/diploma.html" rel="nofollow noreferrer">Evolutionary principles in self-referential learning, or on learning how to learn: The meta-meta-... hook</a> (1987, Schmidhuber's thesis)</p>
  </li>
  <li><p><a href="https://link.springer.com/chapter/10.1007%2F3-540-58484-6_285" rel="nofollow noreferrer">Parallel optimization of evolutionary algorithms</a> (1994, by T. Bäck)</p>
  </li>
  <li><p><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.88.7230&amp;rep=rep1&amp;type=pdf" rel="nofollow noreferrer">Adaptive and Self-adaptive Evolutionary Computations</a> (1995, by Peter Angeline)</p>
  </li>
  <li><p><a href="https://journals.tubitak.gov.tr/elektrik/issues/elk-01-9-1/elk-9-1-2-0008-5.pdf" rel="nofollow noreferrer">Meta-Genetic Programming: Co-evolving the Operators of Variation</a> (1998, by B. Edmonds)</p>
  </li>
  </ul>
  <p>More generally, the optimization of an optimization algorithm is known as <a href="https://en.wikipedia.org/wiki/Meta-optimization" rel="nofollow noreferrer">meta-optimization</a> and/or <a href="https://en.wikipedia.org/wiki/Hyperparameter_optimization" rel="nofollow noreferrer">hyper-parameter optimization/tuning</a>, so the idea of meta-optimization is not just restricted to evolutionary algorithms, but can be applied also to e.g. deep learning.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-12-07T17:59:20.933Z
Answers.LastActivityDate: 2020-12-28T18:41:58.503Z
Answers.LastEditDate: 2020-12-28T18:41:58.503Z
Answers.UserDisplayName: nbro
Answers.Body: <p>A genetic algorithm is a class of evolutionary algorithms. </p>
  
  <p>They do get better at searching through the solution possibilities for each trial (generation) over time because evolution usually starts from a population of randomly generated individuals, and is an iterative process. In each generation, the fitness of every individual in the population is evaluated. The more fit individuals are stochastically selected from the current population, and each individual's genome is modified to form a new generation thus getting better and better with each generation over time.</p>
  
  <p>Evolution is defined as the gradual development of something, especially from a simple to a more complex form.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-11-17T23:17:24.400Z
Answers.Text: I think the OP originally meant if the actual algorithms, like the mutation algorithm, also evolves or not, not if genetic algorithms are a form of evolution.
Answers.UserDisplayName: nbro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2017-07-28T08:58:41.403Z
Answers.LastActivityDate: 2019-06-25T20:09:36.293Z
Answers.LastEditDate: 2019-06-25T20:09:36.293Z
Answers.UserDisplayName: Steve Okay
Body: <p>After witnessing the rise of deep learning as automatic feature/pattern recognition over classic machine learning techniques, I had an insight that the more you automate at each level, the better the results, and I, therefore, turned my focus to <a href="https://en.wikipedia.org/wiki/Neuroevolution" rel="nofollow noreferrer">neuroevolution</a>.</p>
  <p>I have been reading neuroevolution publications with the same desire to automate at every level.</p>
  <p>Do genetic algorithms also evolve themselves? Do they get better at searching through the solution space for each generation over time?</p>
  
Tags: <reference-request><genetic-algorithms><evolutionary-algorithms><hyperparameter-optimization><meta-learning>
Title: Do genetic algorithms also evolve?
UserDisplayName: Kayonga Arnauld
ThreadText: # Do genetic algorithms also evolve?
  
  (Asked by: username_0 on 2017-07-27)
  
  After witnessing the rise of deep learning as automatic feature/pattern recognition over classic machine learning techniques, I had an insight that the more you automate at each level, the better the results, and I, therefore, turned my focus to [neuroevolution](https://en.wikipedia.org/wiki/Neuroevolution).
  
  I have been reading neuroevolution publications with the same desire to automate at every level.
  
  Do genetic algorithms also evolve themselves? Do they get better at searching through the solution space for each generation over time?
  
  **Tags:** reference-request, genetic-algorithms, evolutionary-algorithms, hyperparameter-optimization, meta-learning
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2020-12-07. Score: 3
  
  In principle, yes, you can also evolve the genetic algorithm (or, in general, evolutionary algorithm), i.e. you can evolve its operations (such as the mutation and cross-over) and hyper-parameters (such as the size of the population or mutation rate). For example, you could use [genetic programming](http://www.shahed.ac.ir/stabaii/Files/CompIntelligenceBook.pdf) to evolve the cross-over operation of a genetic algorithm. However, these genetic operators and hyper-parameters are usually designed and determined by a human and do not change during the evolution process. Nevertheless, there is a framework for evolutionary computation in which reproduction and mutation can also evolve, known as [auto-constructive evolution](http://faculty.hampshire.edu/lspector/pubs/ace.pdf), and there are other examples in the literature of meta-optimization methods applied to evolutionary algorithms, such as
  
  * [Evolutionary principles in self-referential learning, or on learning how to learn: The meta-meta-... hook](http://people.idsia.ch/%7Ejuergen/diploma.html) (1987, Schmidhuber's thesis)
  * [Parallel optimization of evolutionary algorithms](https://link.springer.com/chapter/10.1007%2F3-540-58484-6_285) (1994, by T. Bäck)
  * [Adaptive and Self-adaptive Evolutionary Computations](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.88.7230&rep=rep1&type=pdf) (1995, by Peter Angeline)
  * [Meta-Genetic Programming: Co-evolving the Operators of Variation](https://journals.tubitak.gov.tr/elektrik/issues/elk-01-9-1/elk-9-1-2-0008-5.pdf) (1998, by B. Edmonds)
  
  More generally, the optimization of an optimization algorithm is known as [meta-optimization](https://en.wikipedia.org/wiki/Meta-optimization) and/or [hyper-parameter optimization/tuning](https://en.wikipedia.org/wiki/Hyperparameter_optimization), so the idea of meta-optimization is not just restricted to evolutionary algorithms, but can be applied also to e.g. deep learning.
  
  
  ---
  
  ### Answer by username_2 on 2017-07-28. Score: 1
  
  A genetic algorithm is a class of evolutionary algorithms.
  
  They do get better at searching through the solution possibilities for each trial (generation) over time because evolution usually starts from a population of randomly generated individuals, and is an iterative process. In each generation, the fitness of every individual in the population is evaluated. The more fit individuals are stochastically selected from the current population, and each individual's genome is modified to form a new generation thus getting better and better with each generation over time.
  
  Evolution is defined as the gradual development of something, especially from a simple to a more complex form.
  
  > **Comments:**
  > **username_1**: I think the OP originally meant if the actual algorithms, like the mutation algorithm, also evolves or not, not if genetic algorithms are a form of evolution.  
  
  ---
  
  