View source for Evolutionary computation

{{Short description|Trial and error problem solvers with a metaheuristic or stochastic optimization character}}
{{For|the journal|Evolutionary Computation (journal)}}
[[File:Darwin image evolution from random patches.gif|thumb|Evolution of a population of random images. Each frame in the animation is a generation showing the best fitness individual with a genome made up of the greyscale level of each patch. Evolution repeatedly follows 1. evaluate fitness, 2. rank individuals and 3. include some genes from next highest fitness individual. Fitness is the error difference with an image of [[Charles Darwin]]. |alt=animation of random patches evolving into image of Charles Darwin]]
{{Evolutionary algorithms}}
{{Evolutionary biology}}
{{Use mdy dates|date=January 2012}}

'''Evolutionary computation''' from [[computer science]] is a family of [[algorithm]]s for [[global optimization]] inspired by [[biological evolution]], and the subfield of [[artificial intelligence]] and [[soft computing]] studying these algorithms. In technical terms, they are a family of population-based [[trial and error]] problem solvers with a [[metaheuristic]] or [[stochastic optimization]] character.

In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes as well as, depending on the method, mixing parental information. In biological terminology, a [[population]] of solutions is subjected to [[natural selection]] (or [[artificial selection]]), [[mutation]] and possibly [[Genetic recombination|recombination]]. As a result, the population will gradually [[evolution|evolve]] to increase in [[fitness (biology)|fitness]], in this case the chosen [[fitness function]] of the algorithm.

Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings, making them popular in [[computer science]]. Many variants and extensions exist, suited to more specific families of problems and data structures. Evolutionary computation is also sometimes used in [[evolutionary biology]] as an ''in silico'' experimental procedure to study common aspects of general evolutionary processes.

== History ==
The concept of mimicking evolutionary processes to solve problems originates before the advent of computers, such as when [[Alan Turing]] proposed a method of genetic search in 1948 .<ref name=":1">{{Citation |last1=Eiben |first1=A. E. |title=Evolutionary Computing: The Origins |date=2015 |url=http://dx.doi.org/10.1007/978-3-662-44874-8_2 |pages=13–24 |place=Berlin, Heidelberg |publisher=Springer Berlin Heidelberg |isbn=978-3-662-44873-1 |access-date=2022-05-06 |last2=Smith |first2=J. E.|series=Natural Computing Series |doi=10.1007/978-3-662-44874-8_2 |url-access=subscription }}</ref> Turing's B-type [[u-machine]]s resemble primitive [[neural network]]s, and connections between neurons were learnt via a sort of [[genetic algorithm]]. His P-type [[u-machine]]s resemble a method for [[reinforcement learning]], where pleasure and pain signals direct the machine to learn certain behaviors. However, Turing's paper went unpublished until 1968, and he died in 1954, so this early work had little to no effect on the field of evolutionary computation that was to develop.<ref name=":2">{{cite arXiv |last1=Burgin |first1=Mark |last2=Eberbach |first2=Eugene |date=2013-04-12 |title=Evolutionary Turing in the Context of Evolutionary Machines |class=cs.AI |eprint=1304.3762 }}</ref>

Evolutionary computing as a field began in earnest in the 1950s and 1960s.<ref name=":1" /> There were several independent attempts to use the process of evolution in computing at this time, which developed separately for roughly 15 years. Three branches emerged in different places to attain this goal: [[Evolution strategy|evolution strategies]], [[evolutionary programming]], and [[genetic algorithm]]s. A fourth branch, [[genetic programming]], eventually emerged in the early 1990s. These approaches differ in the method of selection, the permitted mutations, and the representation of genetic data. By the 1990s, the distinctions between the historic branches had begun to blur, and the term 'evolutionary computing' was  coined in 1991 to denote a field that exists over all four paradigms.<ref name=":0">{{Cite book |url=https://www.worldcat.org/oclc/38270557 |title=Evolutionary computation : the fossil record |date=1998 |publisher=IEEE Press |others=David B. Fogel |isbn=0-7803-3481-7 |location=New York |oclc=38270557}}</ref>

In 1962, [[Lawrence J. Fogel]] initiated the research of [[Evolutionary programming|Evolutionary Programming]] in the United States, which was considered an [[artificial intelligence]] endeavor. In this system, [[Finite-state machine|finite state machines]] are used to solve a prediction problem: these machines would be mutated (adding or deleting states, or changing the state transition rules), and the best of these mutated machines would be evolved further in future generations. The final finite state machine may be used to generate predictions when needed. The evolutionary programming method was successfully applied to prediction problems, system identification, and automatic control. It was eventually extended to handle time series data and to model the evolution of gaming strategies.<ref name=":0" />

In 1964, [[Ingo Rechenberg]] and [[Hans-Paul Schwefel]] introduce the paradigm of [[Evolution strategy|evolution strategies]] in Germany.<ref name=":0" /> Since traditional [[gradient descent]] techniques produce results that may get stuck in local minima, Rechenberg and Schwefel proposed that random mutations (applied to all parameters of some solution vector) may be used to escape these minima. Child solutions were generated from parent solutions, and the more successful of the two was kept for future generations. This technique was first used by the two to successfully solve optimization problems in [[fluid dynamics]].<ref name=":3">{{Citation |last=Fischer |first=Thomas |title=Kybernetische Systemanalyse Einer Tuchfabrik zur Einführung Eines Computergestützten Dispositionssystems der Fertigung |date=1986 |url=http://dx.doi.org/10.1007/978-3-642-71161-9_14 |work=DGOR |pages=120 |place=Berlin, Heidelberg |publisher=Springer Berlin Heidelberg |doi=10.1007/978-3-642-71161-9_14 |isbn=978-3-642-71162-6 |access-date=2022-05-06|url-access=subscription }}</ref> Initially, this optimization technique was performed without computers, instead relying on dice to determine random mutations. By 1965, the calculations were performed wholly by machine.<ref name=":0" />

[[John Henry Holland]] introduced [[genetic algorithm]]s in the 1960s, and it was further developed at the [[University of Michigan]] in the 1970s.<ref name=":4">{{Cite book |last=Mitchell |first=Melanie |url=http://dx.doi.org/10.7551/mitpress/3927.001.0001 |title=An Introduction to Genetic Algorithms |date=1998 |publisher=The MIT Press |doi=10.7551/mitpress/3927.001.0001 |isbn=978-0-262-28001-3}}</ref> While the other approaches were focused on solving problems, Holland primarily aimed to use genetic algorithms to study adaptation and determine how it may be simulated. Populations of chromosomes, represented as bit strings, were transformed by an artificial selection process, selecting for specific 'allele' bits in the bit string. Among other mutation methods, interactions between chromosomes were used to simulate the [[Genetic recombination|recombination]] of DNA between different organisms. While previous methods only tracked a single optimal organism at a time (having children compete with parents), Holland's genetic algorithms tracked large populations (having many organisms compete each generation).

By the 1990s, a new approach to evolutionary computation that came to be called [[genetic programming]] emerged, advocated for by [[John Koza]] among others.<ref name=":0" /> In this class of algorithms, the subject of evolution was itself a program written in a [[high-level programming language]] (there had been some previous attempts as early as 1958 to use machine code, but they met with little success). For Koza, the programs were [[Lisp (programming language)|Lisp]] [[S-expression]]s, which can be thought of as trees of sub-expressions. This representation permits programs to swap subtrees, representing a sort of genetic mixing. Programs are scored based on how well they complete a certain task, and the score is used for artificial selection. Sequence induction, pattern recognition, and planning were all successful applications of the genetic programming paradigm.

Many other figures played a role in the history of evolutionary computing, although their work did not always fit into one of the major historical branches of the field. The earliest computational simulations of [[evolution]] using [[evolutionary algorithm]]s and [[artificial life]] techniques were performed by [[Nils Aall Barricelli]] in 1953, with first results published in 1954.<ref>{{Cite journal |last=Barricelli |first=Nils Aall |date=1954 |title=Esempi Numerici di processi di evoluzione |journal=Methodos |pages=45–68}}</ref> Another pioneer in the 1950s was [[Alex Fraser (scientist)|Alex Fraser]], who published a series of papers on simulation of [[artificial selection]].<ref>{{cite journal |author=Fraser AS |year=1958 |title=Monte Carlo analyses of genetic models |journal=Nature |volume=181 |issue=4603 |pages=208–9 |bibcode=1958Natur.181..208F |doi=10.1038/181208a0 |pmid=13504138 |s2cid=4211563}}</ref> As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs.<ref>{{cite book |last=Koza |first=John R. |title=Genetic Programming: On the Programming of Computers by Means of Natural Selection |publisher=[[MIT Press]] |year=1992 |isbn=978-0-262-11170-6}}</ref> Evolutionary algorithms are now used to solve multi-dimensional problems more efficiently than software produced by human designers, and also to optimize the design of systems.<ref>G. C. Onwubolu and  B V Babu, {{cite book |last1=Onwubolu |first1=Godfrey C. |url=https://www.springer.com/in/book/9783540201670 |title=New Optimization Techniques in Engineering |last2=Babu |first2=B. V. |date=2004-01-21 |publisher=Springer |isbn=9783540201670 |access-date=17 September 2016}}</ref><ref>{{cite journal |author=Jamshidi M |year=2003 |title=Tools for intelligent control: fuzzy controllers, neural networks and genetic algorithms |journal=[[Philosophical Transactions of the Royal Society A]] |volume=361 |issue=1809 |pages=1781–808 |bibcode=2003RSPTA.361.1781J |doi=10.1098/rsta.2003.1225 |pmid=12952685 |s2cid=34259612}}</ref>

== Techniques ==
Evolutionary computing techniques mostly involve [[metaheuristic]] [[Mathematical optimization|optimization]] [[algorithm]]s. Broadly speaking, the field includes:
*[[Agent-based modeling]]
**[[Ant colony optimization]]
**[[Particle swarm optimization]]
**[[Swarm intelligence]]
*[[Artificial immune system]]s
*[[Artificial life]] 
**[[Digital organism]]
*[[Cultural algorithm]]s
*[[Differential evolution]]
*[[Dual-phase evolution]]
*[[Estimation of distribution algorithm]]
*[[Evolutionary algorithm]]
**[[Genetic algorithm]]
**[[Evolutionary programming]]
**[[Genetic programming]]
***[[Gene expression programming]]
***[[Grammatical evolution]]
**[[Evolution strategy]]
*[[Learnable evolution model]]
*[[Learning classifier system]]
*[[Memetic algorithms]]
*[[Neuroevolution]]
*[[Self-organization]] such as [[self-organizing map]]s, [[competitive learning]]
A thorough catalogue with many other recently proposed algorithms has been published in the [https://github.com/fcampelo/EC-Bestiary Evolutionary Computation Bestiary].<ref>{{Cite journal |last1=Campelo |first1=Felipe |last2=Aranha |first2=Claus |date=2018-06-20 |title=Ec Bestiary: A Bestiary Of Evolutionary, Swarm And Other Metaphor-Based Algorithms |url=https://zenodo.org/record/1293035 |language=en |doi=10.5281/ZENODO.1293035}}</ref> It is important to note that many recent algorithms, however, have poor experimental validation.<ref>{{Cite journal |last=Kudela |first=Jakub |date=2022-12-12 |title=A critical problem in benchmarking and analysis of evolutionary computation methods |url=https://www.nature.com/articles/s42256-022-00579-0 |journal=Nature Machine Intelligence |language=en |volume=4 |issue=12 |pages=1238–1245 |arxiv=2301.01984 |doi=10.1038/s42256-022-00579-0 |s2cid=254616518 |issn=2522-5839}}</ref>

== Evolutionary algorithms ==
{{Main|Evolutionary algorithm}}
[[Evolutionary algorithms]] form a subset of evolutionary computation in that they generally only involve techniques implementing mechanisms inspired by [[biological evolution]] such as [[reproduction]], [[mutation]], [[Genetic recombination|recombination]] and [[natural selection]]. [[Candidate solutions]] to the optimization problem play the role of individuals in a population, and the [[Loss function|cost function]] determines the environment within which the solutions "live" (see also [[fitness function]]). [[Evolution]] of the [[Population model (evolutionary algorithm)|population]] then takes place after the repeated application of the above operators.

In this process, there are two main forces that form the basis of evolutionary systems:  '''Recombination''' (e.g. '''crossover''') and '''mutation''' create the necessary diversity and thereby facilitate novelty, while '''selection''' acts as a force increasing quality.

Many aspects of such an evolutionary process are [[stochastic]]. Changed pieces of information due to recombination and mutation are randomly chosen. On the other hand, selection operators can be either deterministic, or stochastic. In the latter case, individuals with a higher [[Fitness function|fitness]] have a higher chance to be selected than individuals with a lower [[Fitness function|fitness]], but typically even the weak individuals have a chance to become a parent or to survive.

=== Evolutionary algorithms and biology ===
{{Main|Evolutionary algorithm}}

[[Genetic algorithms]] deliver methods to model [[biological systems]] and [[systems biology]] that are linked to the theory of [[dynamical systems]], since they are used to predict the future states of the system. This is just a vivid (but perhaps misleading) way of drawing attention to the orderly, well-controlled and highly structured character of development in biology.

However, the use of algorithms and informatics, in particular of [[computational theory]], beyond the analogy to dynamical systems, is also relevant to understand evolution itself.

This view has the merit of recognizing that there is no central control of development; organisms develop as a result of local interactions within and between cells. The most promising ideas about program-development parallels seem to us to be ones that point to an apparently close analogy between processes within cells, and the low-level operation of modern computers.<ref>{{Cite book | chapter-url=https://plato.stanford.edu/entries/information-biological/#InfEvo | title=The Stanford Encyclopedia of Philosophy| chapter=Biological Information| publisher=Metaphysics Research Lab, Stanford University| year=2016}}</ref> Thus, biological systems are like computational machines that process input information to compute next states, such that biological systems are closer to a computation than classical dynamical system.<ref>{{cite journal |author= J.G. Diaz Ochoa |title= Elastic Multi-scale Mechanisms: Computation and Biological Evolution |journal=[[Journal of Molecular Evolution]] |volume=86 |issue=1 |pages=47–57 |year=2018 |pmid=29248946 |doi=10.1007/s00239-017-9823-7 |bibcode=2018JMolE..86...47D |s2cid= 22624633 }}</ref>

Furthermore, following concepts from [[computational theory]], micro processes in biological organisms are fundamentally incomplete and undecidable ([[completeness (logic)]]), implying that “there is more than a crude metaphor behind the analogy between cells and computers.<ref>{{cite journal |author= A. Danchin |title= Bacteria as computers making computers |journal=[[FEMS Microbiol. Rev.]] |volume=33 |issue=1 |pages=3–26 |year=2008 |doi=10.1111/j.1574-6976.2008.00137.x |pmid= 19016882 |pmc=2704931 }}</ref>

The analogy to computation extends also to the relationship between [[inheritance systems]] and biological structure, which is often thought to reveal one of the most pressing problems in explaining the origins of life.

''Evolutionary automata''{{r|ldr11|ldr13|ldr14}}, a generalization of ''Evolutionary Turing machines''{{r|ldr15|ldr16}}, have been introduced in order to investigate more precisely properties of biological and evolutionary computation. In particular, they allow to obtain new results on expressiveness of evolutionary computation{{r|ldr14|ldr17}}. This confirms the initial result about undecidability of natural evolution and evolutionary algorithms and processes. ''Evolutionary finite automata'', the simplest subclass of Evolutionary automata working in ''terminal mode'' can accept arbitrary languages over a given alphabet, including non-recursively enumerable (e.g., diagonalization language) and recursively enumerable but not recursive languages (e.g., language of the universal Turing machine){{r|ldr18}}.

== Notable practitioners ==
The list of active researchers is naturally dynamic and non-exhaustive. A network analysis of the community was published in 2007.<ref>{{cite arXiv |author=J.J. Merelo and C. Cotta |title=Who is the best connected EC researcher? Centrality analysis of the complex network of authors in evolutionary computation |year=2007 |eprint=0708.2021|class=cs.CY }}</ref>

* [[Kalyanmoy Deb]]
* [[Kenneth A De Jong]]
* [[Peter J. Fleming]]
* [[David B. Fogel]]
* [[Stephanie Forrest]]
* [[David E. Goldberg]]
* [[John Henry Holland]]
* [[Theo Jansen]]
* [[John Koza]]
* [[Zbigniew Michalewicz]]
* [[Melanie Mitchell]]
* [[Peter Nordin]]
* [[Riccardo Poli]]
* [[Ingo Rechenberg]]
* [[Hans-Paul Schwefel]]

==Publications==
===Journals===
While articles on or using evolutionary computation permeate the literature, several journals are dedicated to evolutionary computation:
* [[Evolutionary Computation (journal)]] (founded 1993, [[MIT Press]])
* [[Artificial Life (journal)]] (founded 1993, [[MIT Press]])
* [[IEEE Transactions on Evolutionary Computation]] (founded 1997, [[IEEE]])
* Genetic Programming and Evolvable Machines (founded 2000, [[Springer Nature]])
* [[Swarm Intelligence]] (founded 2007, [[Springer Nature]])
* Evolutionary Intelligence (founded 2008, [[Springer Nature]])
* Journal of Artificial Evolution and Applications  (2008–2010, [[Hindawi (publisher)| Hindawi]])
* [[Memetic Computing]] (founded 2009, [[Springer Nature]])
* International Journal of Applied Evolutionary Computation (founded 2010, [[:fr:IGI Global]])
* Swarm and Evolutionary Computation (founded 2011, [[Elsevier]]) 
* [[International Journal of Swarm Intelligence and Evolutionary Computation]] (founded 2012, [[OMICS Publishing Group|Walsh Medical Media]])

===Conferences===
The main conferences in the evolutionary computation area include 
* [[Association for Computing Machinery|ACM]] [[Genetic and Evolutionary Computation Conference]] (GECCO), 
* [[IEEE Congress on Evolutionary Computation]] (CEC), 
* [[EvoStar]], which comprises four conferences: EuroGP, EvoApplications, EvoCOP and EvoMUSART, 
* [[Parallel Problem Solving from Nature]] (PPSN).

== See also ==
{{div col|colwidth=40em}}
* Adaptive dimensional search
* [[Artificial development]]
* [[Autoconstructive]]
* [[Developmental biology]]
* [[Digital organism]]
* [[Estimation of distribution algorithm]]
* [[Evolutionary robotics]]
* [[Evolved antenna]]
* [[Fitness approximation]]
* [[Fitness function]]
* [[Fitness landscape]]
* [[Genetic operators]]
* [[Grammatical evolution]]
* [[Human-based evolutionary computation]]
* [[Inferential programming]]
* [[Interactive evolutionary computation]]
* [[List of digital organism simulators]]
* [[Mutation testing]]
* [[No free lunch in search and optimization]]
* [[Program synthesis]]
* [[Test functions for optimization]]
* [[Unconventional computing]]
* [[Universal Darwinism]]
{{div col end}}

== References ==

{{reflist|refs=
<ref name="ldr11">{{Cite book |doi = 10.1007/978-3-642-29694-9_9|isbn = 978-3-642-29693-2|chapter = Recursively Generated Evolutionary Turing Machines and Evolutionary Automata |editor=Xin-She Yang |title = Artificial Intelligence, Evolutionary Computing and Metaheuristics|series = Studies in Computational Intelligence|year = 2013|last1 = Burgin|first1 = Mark|last2 = Eberbach|first2 = Eugene|volume = 427|pages = 201–230 |publisher=Springer-Verlag}}</ref>
<ref name="ldr13">Burgin, M. and Eberbach, E. (2010)  Bounded and Periodic Evolutionary Machines, in Proc. 2010 Congress on Evolutionary Computation (CEC'2010), Barcelona, Spain, 2010, pp. 1379–1386</ref>
<ref name="ldr14">{{Cite journal |doi = 10.1093/comjnl/bxr099|title = Evolutionary Automata: Expressiveness and Convergence of Evolutionary Computation|year = 2012|last1 = Burgin|first1 = M.|last2 = Eberbach|first2 = E.|journal = The Computer Journal|volume = 55|issue = 9|pages = 1023–1029}}</ref>
<ref name="ldr15">Eberbach E. (2002) On Expressiveness of Evolutionary Computation: Is EC Algorithmic?, Proc. 2002 World Congress on Computational Intelligence WCCI’2002, Honolulu, HI, 2002, 564–569.</ref>
<ref name="ldr16">Eberbach, E. (2005) Toward a theory of evolutionary computation, BioSystems, v. 82, pp. 1-19.</ref>
<ref name="ldr17">{{Cite book |doi = 10.1109/CEC.2009.4983207|isbn = 978-1-4244-2958-5|chapter = Evolutionary automata as foundation of evolutionary computation: Larry Fogel was right|title = 2009 IEEE Congress on Evolutionary Computation|year = 2009|last1 = Eberbach|first1 = Eugene|last2 = Burgin|first2 = Mark|pages = 2149–2156|publisher=IEEE|s2cid = 2869386}}</ref>
<ref name="ldr18">Hopcroft, J.E., R. Motwani, and J.D. Ullman (2001) Introduction to Automata Theory, Languages, and Computation, Addison Wesley, Boston/San Francisco/New York</ref>
}}

== Bibliography ==
* Th. Bäck, D.B. Fogel, and [[Zbigniew Michalewicz|Z. Michalewicz]] (Editors), [https://www.amazon.com/Handbook-Evolutionary-Computation-Thomas-Back/dp/0750303921 Handbook of Evolutionary Computation], 1997, {{ISBN|0750303921}}
* Th. Bäck and H.-P. Schwefel. [http://caribou.iisg.agh.edu.pl/pub/svn/age/jage/legacy/papers/mgrKA/pdf/evco.1993.1.1.pdf An overview of evolutionary algorithms for parameter optimization]. {{Webarchive|url=https://web.archive.org/web/20180712174303/http://caribou.iisg.agh.edu.pl/pub/svn/age/jage/legacy/papers/mgrKA/pdf/evco.1993.1.1.pdf |date=July 12, 2018 }} Evolutionary Computation, 1(1):1–23, 1993.
* W. Banzhaf, P. Nordin, R.E. Keller, and F.D. Francone. Genetic Programming — An Introduction. Morgan Kaufmann, 1998.
* S. Cagnoni, et al., [https://www.springer.com/computer+science/theoretical+computer+science/foundations+of+computations/book/978-3-540-67353-8 Real-World Applications of Evolutionary Computing], Springer-Verlag [[Lecture Notes in Computer Science]], Berlin, 2000.
* R. Chiong, Th. Weise, [[Zbigniew Michalewicz|Z. Michalewicz]] (Editors), [https://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-23423-1 Variants of Evolutionary Algorithms for Real-World Applications], [[Springer Publishing|Springer]], 2012, {{ISBN|3642234232}}
* K. A. De Jong, Evolutionary computation: a unified approach. [[MIT Press]], Cambridge MA, 2006
* A. E. Eiben and J.E. Smith, [https://www.nature.com/articles/nature14544?proof=true19 From evolutionary computation to the evolution of things], Nature, 521:476-482, doi:10.1038/nature14544, 2015
* A. E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer, [https://www.springer.com/computer/theoretical+computer+science/book/978-3-540-40184-1 First edition], 2003; [https://www.springer.com/cn/book/9783662448731 Second edition], 2015
* D. B. Fogel. Evolutionary Computation. [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.969.3703&rep=rep1&type=pdf Toward a New Philosophy of Machine Intelligence]. IEEE Press, Piscataway, NJ, 1995.
* L. J. Fogel, A. J. Owens, and M. J. Walsh. [https://cds.cern.ch/record/107769 Artificial Intelligence through Simulated Evolution]. New York: John Wiley, 1966.
* D. E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, 1989.
* J. H. Holland. Adaptation in natural and artificial systems. [[University of Michigan Press]], Ann Arbor, 1975.
* P. Hingston, L. Barone, and [[Zbigniew Michalewicz|Z. Michalewicz]] (Editors), [https://www.springer.com/computer/ai/book/978-3-540-74109-1 Design by Evolution, Natural Computing Series], 2008, [[Springer Publishing|Springer]], {{ISBN|3540741097}}
* J. R. Koza. Genetic Programming: On the Programming of Computers by means of Natural Evolution. MIT Press, Massachusetts, 1992.
* F.J. Lobo, C.F. Lima, [[Zbigniew Michalewicz|Z. Michalewicz]] (Editors), [https://www.amazon.com/Parameter-Evolutionary-Algorithms-Computational-Intelligence/dp/3642088929/ Parameter Setting in Evolutionary Algorithms], [[Springer Publishing|Springer]], 2010, {{ISBN|3642088929}}
* [[Zbigniew Michalewicz|Z. Michalewicz]], [https://www.springer.com/computer/ai/book/978-3-540-60676-5 Genetic Algorithms + Data Structures – Evolution Programs], 1996, [[Springer Publishing|Springer]], {{ISBN|3540606769}}
*  [[Zbigniew Michalewicz|Z. Michalewicz]] and D.B. Fogel, [https://www.springer.com/computer/theoretical+computer+science/book/978-3-540-22494-5 How to Solve It: Modern Heuristics], [[Springer Publishing|Springer]], 2004, {{ISBN|978-3-540-22494-5}}
* I. Rechenberg. Evolutionstrategie: Optimierung Technischer Systeme nach Prinzipien des Biologischen Evolution. Fromman-Hozlboog Verlag, Stuttgart, 1973. {{in lang|de}}
* H.-P. Schwefel. Numerical Optimization of Computer Models. John Wiley & Sons, New-York, 1981. 1995 – 2nd edition.
* D. Simon. [http://academic.csuohio.edu/simond/EvolutionaryOptimization Evolutionary Optimization Algorithms] {{Webarchive|url=https://web.archive.org/web/20140310010900/http://academic.csuohio.edu/simond/EvolutionaryOptimization/ |date=March 10, 2014 }}. Wiley, 2013.
* {{cite journal |author1=M. Sipper |author2=W. Fu |author3=K. Ahuja |author4=J. H. Moore |title=Investigating the parameter space of evolutionary algorithms|journal=BioData Mining|volume=11|pages=2|doi=10.1186/s13040-018-0164-x|pmid=29467825|pmc=5816380|year=2018 |doi-access=free }}
* {{cite arXiv |author1=Y. Zhang |author2=S. Li. |title=PSA: A novel optimization algorithm based on survival rules of porcellio scaber |eprint=1709.09840 |class=cs.NE |year=2017 }}

== External links ==
*[https://plato.stanford.edu/entries/information-biological/#InfEvo/ Article in the Stanford Encyclopedia of Philosophy about Biological Information (English)]

<br />{{Evolutionary computation}}
{{Authority control}}

[[Category:Evolutionary computation| ]]
[[Category:Evolution]]