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Nature's algorithms [genetic algorithms]

J. Carnahan, R. Sinha
2001 IEEE potentials  
Evolution and the Genetic Algorithm As their name suggests, genetic algorithms attempt to mimic the process of biological evolution in developing a solution.  ...  Clearly, simulated annealing produces the lowest-cost tours, followed by genetic algorithms.  ... 
doi:10.1109/45.954644 fatcat:bt4mxargcrfajigjc7nme67gui

Genetic algorithms

Alexander O. Skomorokhov
1996 ACM SIGAPL APL Quote Quad  
This paper describes an application oriented approach to the genetic-algorithm technique.  ...  APL96, Lancaster, England 97 Genetic Algorithms . . . APL96, Lancaster, England  ...  Genetic algorithms search for the best alternative (in the sense of a given fitness function) through chromosomes' evolution. Basic steps in genetic algorithms are the following.  ... 
doi:10.1145/253417.253399 fatcat:4ml2mil2vrfhpgb35ctdnql6w4

Genetic algorithms

Alexander O. Skomorokhov
1996 Proceedings of the conference on Designing the future - APL '96  
This paper describes an application oriented approach to the genetic-algorithm technique.  ...  APL96, Lancaster, England 97 Genetic Algorithms . . . APL96, Lancaster, England  ...  Genetic algorithms search for the best alternative (in the sense of a given fitness function) through chromosomes' evolution. Basic steps in genetic algorithms are the following.  ... 
doi:10.1145/253341.253399 dblp:conf/apl/Skomorokhov96 fatcat:krpidpvelvcsdb53mkorkcz6dq

Genetic Algorithms [chapter]

Linda L. Hill, Mehmet M. Dalkiliç, Brahim Medjahed, Mourad Ouzzani, Ahmed K. Elmagarmid, Joseph M. Hellerstein, Colin R. Reeves, Christopher B. Jones, Ross S. Purves, Michael F. Goodchild, Jayant Sharma, John Herring (+24 others)
2009 Encyclopedia of Database Systems  
algorithms.  ...  The term genetic algorithm, almost universally abbreviated nowadays to GA, was first used by John Holland [1], whose book Adaptation in Natural and Aritificial Systems of 1975 was instrumental in creating  ...  Perhaps the most fundamental characteristic of genetic algorithms is that their use of populations of many strings.  ... 
doi:10.1007/978-0-387-39940-9_562 fatcat:kez3ycuikfexxeridfim4onnom

Genetic Algorithm

Hidehiko OKABE
1991 Journal of Japan Society for Fuzzy Theory and Systems  
綿 黼 Genetic Algorithm 岡 部 秀 彦 * 1. は じめ に 最 近 に な っ て 日 本 で も Genetic Algorithm ( 以 後 GA と 略 す )と い う言 葉 を 目 に す る よ う に な っ た 。  ...  Algorithm う。  ... 
doi:10.3156/jfuzzy.3.4_2 fatcat:uisanryxyvhenpftnni6ic57hq

Practical genetic algorithms

2005 Discrete Applied Mathematics  
The Binary Genetic Algorithm 3. The Continuous Genetic Algorithm 4. Basic Applications 5. An Added Level of Sophistication 6. Advanced Applications 7.  ...  More Natural Optimization Algorithms Appendix I Test Functions Appendix II MATLAB Code Appendix III High-Performance Fortran Code Glossary Index 0166-  ... 
doi:10.1016/j.dam.2004.09.005 fatcat:vk2c5pbuhrdgtna33mnnvv4eky

Genetic algorithm theory

Jonathan E. Rowe
2008 Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation - GECCO '08  
A basic object of study is the Simple Genetic Algorithm. At any time-step (or generation) there is a population (of size N) of binary strings (of length ).  ...  Genetic Algorithm Theory GECCO 2012 4 / 1 Producing the next population To produce the next population we follow these steps N times: 1 Select two items from the population. 2 Cross them over to form an  ...  Rowe (University of Birmingham, UK) Genetic Algorithm Theory GECCO 2012 73 / 1 Jonathan E. Rowe (University of Birmingham, UK) Genetic Algorithm Theory GECCO 2012 74 / 1  ... 
doi:10.1145/1388969.1389067 dblp:conf/gecco/Rowe08 fatcat:l7kolsvjdvaztl5wlgv2qgnlqu

Genetic algorithm theory

Jonathan E. Rowe
2012 Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion '12  
An equivalent way of characterising a single generation of the Simple Genetic Algorithm is as follows:  ...  Rowe (University of Birmingham, UK) Genetic Algorithm Theory GECCO 2012 33 / 1 Jonathan E. Rowe (University of Birmingham, UK) Genetic Algorithm Theory GECCO 2012 24 / 1 / 1 / 1  ...  Rowe (University of Birmingham, UK) Genetic Algorithm Theory GECCO 2012 34 / 1 Metastable states The states in which the algorithm spends a lot of its time can sometimes be found by analysing the fixed-points  ... 
doi:10.1145/2330784.2330923 dblp:conf/gecco/Rowe12 fatcat:x73wrzi2crbpdlslv7aiiwia5u

Practical Genetic Algorithms

2005 Technometrics  
This paper offers practical design-guidelines for developing efficient genetic algorithms (GAs) to successfully solve realworld problems.  ...  INTRODUCTION Over the last decade, genetic algorithms (GAs) have been successfully applied to problems in business, engineering, and science [1, 2, 3] .  ...  Genetic operators: The genetic operators must be carefully designed as they directly affect the performance of GAs. Selection focuses on the exploration of promising regions in the solution space.  ... 
doi:10.1198/tech.2005.s274 fatcat:jbria7vfgjcqhgum4oqhkjhowu

Homogeneous genetic algorithms

Alexander Stanoyevitch
2010 International Journal of Computer Mathematics  
In this note, we briefly describe a new type of genetic algorithm that is designed to mitigate one or both of the following two major difficulties that traditional genetic algorithms may suffer: 1.  ...  Homogeneous GAs have significantly outperformed traditional genetic algorithms for some typical problems in which these difficulties arise.  ...  ELEMENTS OF THE ALGORITHM For a given discrete optimization problem, in a homogeneous GA, elements of the feasible solution set are represented by their corresponding (unordered) set of active genes.  ... 
doi:10.1080/00207160801968770 fatcat:2rzkwanrj5fktgsexd4gbfuna4

Homogeneous genetic algorithms

Alexander Stanoyevitch
2007 Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07  
In this note, we briefly describe a new type of genetic algorithm that is designed to mitigate one or both of the following two major difficulties that traditional genetic algorithms may suffer: 1.  ...  Homogeneous GAs have significantly outperformed traditional genetic algorithms for some typical problems in which these difficulties arise.  ...  ELEMENTS OF THE ALGORITHM For a given discrete optimization problem, in a homogeneous GA, elements of the feasible solution set are represented by their corresponding (unordered) set of active genes.  ... 
doi:10.1145/1276958.1277261 dblp:conf/gecco/Stanoyevitch07 fatcat:3nzbt6ebyzeyhoeaebohfhijou

Statistical Genetic Algorithm

Mohammad Ali Tabarzad, Caro Lucas, Ali Hamzeh
2008 Zenodo  
This algorithms is called Statistical Genetic Algorithm and is compared with traditional GA in some benchmark problems.  ...  Adaptive Genetic Algorithms extend the Standard Gas to use dynamic procedures to apply evolutionary operators such as crossover, mutation and selection.  ...  algorithm parameters may really improve the genetic algorithm performance.  ... 
doi:10.5281/zenodo.1330822 fatcat:3cnidf4gkbauzec3lshp25b4cq

Fast Genetic Algorithms [article]

Benjamin Doerr, Huu Phuoc Le, Régis Makhmara, Ta Duy Nguyen
2017 arXiv   pre-print
For genetic algorithms using a bit-string representation of length n, the general recommendation is to take 1/n as mutation rate.  ...  Following the example of fast simulated annealing, fast evolution strategies, and fast evolutionary programming, we propose to call genetic algorithms using a heavy-tailed mutation operator fast genetic  ...  The first work in this direction [JW05] shows that a simple (µ + 1) genetic algorithm with appropriate parameter settings can obtain a better runtime than mutation-based algorithms.  ... 
arXiv:1703.03334v2 fatcat:2rjgzqs52fbgholexxudmwnrge

Genetic clustering algorithm

M. A. Anfyorov
2020 Российский технологический журнал  
Moreover, this is due to the absence of the genetic operator of inversion in this algorithm. Secondly, a new genetic operator used for filtering has been implemented.  ...  The genetic algorithm of clustering of analysis objects in different data domains has been offered within the hybrid concept of intelligent information technologies development aimed to support decision-making  ...  Genetic clustering algorithm Rossiiskii tekhnologicheskii zhurnal = Russian Technological Journal. 2019;7(6):134-150 (in Russ.). https://doi.org/10.32362/2500-316X-2019-7-6-134-150 Шаг 4 . 4 Необходимую  ... 
doi:10.32362/2500-316x-2019-7-6-134-150 fatcat:kyxqycrmanfnfbcthbeqpkadbu

Modern Genetic Algorithms

In-Ho LEE
2018 Physics and High Technology  
Repeated objective function evaluation for complex problems is often the most prohibitive and limiting segment of genetic algorithms.  ... 
doi:10.3938/phit.27.002 fatcat:riqzpdwjqzbrbhupxh5qarig7m
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