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In computer science and operations research, '''genetic algorithms''' or '''GAs''' view learning as a competition among a population of evolving candidate problem solutions. A 'fitness' function estimates each solution for deciding whether it will contribute to the next generation of solutions or not. After that, as in gene transfer in sexual reproduction, the algorithm creates a new population of candidate solutions.
In computer science and operations research, '''genetic algorithms''' or '''GAs''' view learning as a competition among a population of evolving candidate problem solutions. A 'fitness' function estimates each solution for deciding whether it will contribute to the next generation of solutions or not. After that, as in gene transfer in sexual reproduction, the algorithm creates a new population of candidate solutions.


A genetic algorithm is a meta-heuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms. Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of their applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, and causal inference.
A genetic algorithm is a meta-heuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms<ref name=WPEA>Wikipedia has details [[Wikipedia:Evolutionary algorithm|about evolutionary algorithms]].</ref>. Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of their applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, and causal inference.
 
Wikipedia has a lot more information on genetic algorithms.<ref name=WPGA>Wikipedia has much more detail [[Wikipedia:Genetic algorithm|on genetic algorithms]].</ref>


==Attribution==
==Attribution==
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</references>
*Barricelli, Nils Aall (1954), ''Esempi numerici di processi di evoluzione,''  Methodos, pp. 45-68.
*Barricelli, Nils Aall (1963), ''Numerical testing of evolution theories.  Part II. Preliminary tests of performance, symbiogenesis and terrestrial life,'' Acta Biotheoretica,  16: 99-126.
*Bies, Robert R; Muldoon, Matthew F; Pollock, Bruce G; Manuck, Steven; Smith, Gwenn and Sale, Mark E(2006), ''A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection'' Journal of Pharmacokinetics and Pharmacodynamics Springer-Netherlands pp. 196-221
*Crosby, Jack L. (1973), ''Computer Simulation in Genetics,''  John Wiley & Sons, London.
*Falkenauer, Emanuel (1997), ''Genetic Algorithms and Grouping Problems,'' John Wiley & Sons Ltd, Chichester, England. ISBN 978-0-471-97150-4
*Fentress, Sam W (2005), ''Exaptation as a means of evolving complex solutions,'' MA Thesis, University of Edinburgh. ([http://www.inf.ed.ac.uk/publications/thesis/online/IM050329.pdf pdf])
*Fogel, David B. (2000) ''Evolutionary Computation: Towards a New Philosophy of Machine Intelligence'' IEEE Press, New York.
*Fogel, David B. (editor) (1998) ''Evolutionary Computation: The Fossil Record,'' IEEE Press, New York.
*Fraser, Alex S. (1957), ''Simulation of Genetic Systems by Automatic Digital Computers. I. Introduction.''  Australian Journal of Biological Sciences  vol. 10  484-491.
*Fraser, Alex and Donald Burnell (1970), ''Computer Models in Genetics,''  McGraw-Hill, New York.
*Goldberg, David E (1989), ''Genetic Algorithms in Search, Optimization and Machine Learning,'' Kluwer Academic Publishers, Boston, MA.
*Goldberg, David E (2002), ''The Design of Innovation: Lessons from and for Competent Genetic Algorithms,'' Addison-Wesley, Reading, MA.
*Holland, John H (1975), ''Adaptation in Natural and Artificial Systems'', University of Michigan Press, Ann Arbor
*Kjellström, G. Optimization of electrical Networks with respect to Tolerance Costs. Ericsson Technics, no. 3, pp. 157-175, 1970.
*Kjellström, G. Evolution as a statistical optimization algorithm. Evolutionary Theory 11:105-117 (January, 1996).
*Koza, John (1992), ''Genetic Programming: On the Programming of Computers by Means of Natural Selection'', [[MIT Press]]. ISBN 0-262-11170-5
*Michalewicz, Zbigniew (1999), ''Genetic Algorithms + Data Structures = Evolution Programs'', Springer-Verlag.
*Mitchell, Melanie, (1996), ''An Introduction to Genetic Algorithms'', MIT Press, Cambridge, MA.
*Rechenberg, Ingo (1971): Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis). Reprinted by Fromman-Holzboog (1973).
*Schmitt, Lothar M, Nehaniv Chrystopher N, Fujii Robert H (1998), ''Linear analysis of genetic algorithms'', Theoretical Computer Science (208), pp. 111-148
*Schmitt, Lothar M (2001),  ''Theory of Genetic Algorithms'', Theoretical Computer Science (259), pp. 1-61
*Schmitt, Lothar M (2004),  ''Theory of Genetic Algorithms II: models for genetic operators over the string-tensor representation of populations and convergence to global optima for arbitrary fitness function under scaling'', Theoretical Computer Science (310), pp. 181-231
* Schwefel, Hans-Paul (1974): Numerische Optimierung von Computer-Modellen (PhD thesis). Reprinted by Birkhäuser (1977).
*Syswerda G. (1989) Uniform crossover in genetic algorithms. In J. D. Schaffer, editor, '' Proceedings of the Third International Conference on Genetic Algorithms.'' Morgan Kaufmann.
*Vose, Michael D (1999), ''The Simple Genetic Algorithm: Foundations and Theory'', MIT Press, Cambridge, MA.
*Whitley, D. (1994). ''A genetic algorithm tutorial''. Statistics and Computing 4, 65–85.
*Wright, A.H. et al. (2003) Implicit Parallelism in ''Proceedings of the Genetic and Evolutionary Computation Conference'' 2003
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In computer science and operations research, genetic algorithms or GAs view learning as a competition among a population of evolving candidate problem solutions. A 'fitness' function estimates each solution for deciding whether it will contribute to the next generation of solutions or not. After that, as in gene transfer in sexual reproduction, the algorithm creates a new population of candidate solutions.

A genetic algorithm is a meta-heuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms[1]. Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of their applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, and causal inference.

Wikipedia has a lot more information on genetic algorithms.[2]

Attribution

Some content on this page may previously have appeared on Wikipedia.

Footnotes

  1. ↑ Wikipedia has details about evolutionary algorithms.
  2. ↑ Wikipedia has much more detail on genetic algorithms.

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