## A Genetic Algorithm Tutorial (1994)

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Venue: | Statistics and Computing |

Citations: | 234 - 5 self |

### BibTeX

@ARTICLE{Whitley94agenetic,

author = {Darrell Whitley},

title = {A Genetic Algorithm Tutorial},

journal = {Statistics and Computing},

year = {1994},

volume = {4},

pages = {65--85}

}

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### Abstract

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search byhyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.

### Citations

7363 |
Genetic Algorithms
- Goldberg
- 1989
(Show Context)
Citation Context ...ation after several generations, and the selective pressure based on tness is correspondingly reduced. This problem can partially be addressed by using some form of tness scaling (Grefenstette, 1986� =-=Goldberg, 1989-=-). In the simplest case, one can subtract the evaluation of the worst string in the population from the evaluations of all strings in the population. One can now compute the average string evaluation ... |

1970 | Genetic algorithms + data structures = evolution programs, third edn - Michalewicz - 1996 |

1176 | Handbook of Genetic Algorithms - DAVIS - 1991 |

865 |
An Analysis of the Behavior of a Class of Genetic Adaptive Systems
- Jong
- 1975
(Show Context)
Citation Context ..., the term genetic algorithm has two meanings. In a strict interpretation, the genetic algorithm refers to a model introduced and investigated by John Holland (1975) and by students of Holland (e.g., =-=DeJong, 1975-=-). It is still the case that most of the existing theory for genetic algorithms applies either solely or primarily to the model introduced by Holland, as well as variations on what will be referred to... |

662 | Evolutionsstrategie: optimierung technischer systeme nach prinzipien der biologischen evolution - Rechenberg - 1973 |

646 | Intelligence through simulated evolution. Forty years of evolutionary programming - Fogel - 1999 |

527 | Uniform Crossover in Genetic Algorithms - Syswerda - 1989 |

511 | Numerical optimization of computer models - Schwefel - 1981 |

440 |
Co-evolving Parasites Improve Simulated Evolution as an Optimization Procedure
- Hillis
- 1992
(Show Context)
Citation Context ...nly one o spring is produced. and becomes the new resident at that processor. Several people have proposed this type of computational model (Manderick and Spiessens, 1989� Collins and Je erson, 1991� =-=Hillis, 1990-=-� Davidor, 1991). The common theme in cellular genetic algorithms is that selection and mating are typically restricted to a local neighborhood. There are no explicit islands in the model, but there i... |

392 | A comparative analysis of selection schemes used in genetic algorithms,” in Foundations of Genetic Algorithms
- Goldberg, Deb
- 1991
(Show Context)
Citation Context ...rect way to implement a parallel genetic algorithm is to implement something close to a canonical genetic algorithm. The only change is that selection is done by tournament selection (Goldberg, 1990; =-=Goldberg and Deb, 1991-=-). Tournament selection implements a noisy form of ranking. Recall that the implementation of one generation in a canonical genetic algorithm can be seen as a two-step process. First, selection is use... |

385 |
Optimization of control parameters for genetic algorithms
- Grefenstette
- 1986
(Show Context)
Citation Context ...ividual in the population after several generations, and the selective pressure based on tness is correspondingly reduced. This problem can partially be addressed by using some form of tness scaling (=-=Grefenstette, 1986-=-� Goldberg, 1989). In the simplest case, one can subtract the evaluation of the worst string in the population from the evaluations of all strings in the population. One can now compute the average st... |

322 |
Reducing bias and inefficiency in the selection algorithm
- Baker
- 1987
(Show Context)
Citation Context ...around the pie with N equally spaced pointers. A single spin of the roulette wheel will now simultaneously pick all N members of the intermediate population. The resulting selection is also unbiased (=-=Baker, 1987-=-). After selection has been carried out the construction of the intermediate population is complete and recombination can occur. This can be viewed as creating the next population from the intermediat... |

243 | A connectionist machine for genetic hillclimbing - Ackley - 1987 |

227 | A survey of evolution strategies - Bäck, Hoffmeister, et al. - 1991 |

212 |
Adaptive selection methods for genetic algorithms
- Baker
- 1985
(Show Context)
Citation Context ...fi=f where fi is the evaluation associated with string i and f is the average evaluation of all the strings in the population. Fitness can also be assigned based on a string's rank in the population (=-=Baker, 1985-=-� Whitley, 1989) or by sampling methods, such as tournament selection (Goldberg, 1990). It is helpful to view the execution of the genetic algorithm as a two stage process. It starts with the current ... |

205 |
Distributed genetic algorithms
- Tanese
- 1989
(Show Context)
Citation Context ...erhaps every ve generations or so, the subpopulations would swap a few strings. This migration allows subpopulations to share genetic material (Whitley and Starkweather, 1990� Gorges-Schleuter, 1991� =-=Tanese 1989-=-� Starkweather et al., 1991.) Assume for a moment that one executes 16 separate genetic algorithms, each using a population of 100 strings without migration. In this case, 16 independent searches occu... |

195 |
Modelling a genetic algorithm with markov chains
- Nix, Vose
- 1992
(Show Context)
Citation Context ...ixing matrix M which not only includes probabilities for 1-point crossover, but also mutation. More complex extension of the Vose and Liepins model include nite population models using Markov chains (=-=Nix and Vose, 1992-=-). Vose (1993) surveys the current state of this research. 288 Other Models of Evolutionary Computation There are several population based algorithms that are either spin-o s of Holland's genetic alg... |

171 | Adaptation in Natural and Arti cial Systems. The - Holland - 1975 |

163 |
The Simple Genetic Algorithm
- Vose
- 1999
(Show Context)
Citation Context ...ions on what will be referred to in this paper as the canonical genetic algorithm. Recent theoretical advances in modelling genetic algorithms also apply primarily to the canonical genetic algorithm (=-=Vose, 1993-=-). In a broader usage of the term, a genetic algorithm is any population-based model that uses selection and recombination operators to generate new sample points in a search space. Many genetic algor... |

144 |
Fine-grained parallel genetic algorithms
- Manderick, Spiessens
- 1989
(Show Context)
Citation Context ...al probabilistic selection could be used. In either case, only one o spring is produced. and becomes the new resident at that processor. Several people have proposed this type of computational model (=-=Manderick and Spiessens, 1989-=-� Collins and Je erson, 1991� Hillis, 1990� Davidor, 1991). The common theme in cellular genetic algorithms is that selection and mating are typically restricted to a local neighborhood. There are no ... |

133 | Genetic algorithms for real parameter optimization," Proceedings of Foundations of Genetic Algorithms,(edited by Gregory - Wright - 1991 |

128 | Selection in massively parallel genetic algorithms
- Collins, Jefferson
- 1991
(Show Context)
Citation Context ...4 Whitley In either case, only one offspring is produced, and becomes the new resident at that processor. Several people have proposed this type of computational model (Manderick and Spiessens, 1989; =-=Collins and Jefferson, 1991-=-; Hillis, 1990; Davidor, 1991). The common theme in cellular genetic algorithms is that selection and mating are typically restricted to a local neighbourhood. There are no explicit islands in the mod... |

125 | Simple genetic algorithms and the minimal, deceptive problem - Goldberg - 1987 |

108 |
How genetic algorithms work: A critical look at implicit parallelism
- Grefenstette, Baker
- 1989
(Show Context)
Citation Context ...itions of hyperspace. Thus, looking at the average tness of all the strings in a particular hyperplane (or using a random sample to estimate this tness) is only relevant to the rst generation or two (=-=Grefenstette and Baker, 1989-=-). After this, the sampling of strings is biased and the inexactness of the schema theorem makes it impossible to predict computational behavior. In general, the schema theorem provides a lower bound ... |

108 | Evolution in time and space – the parallel genetic algorithm
- Mühlenbein
- 1991
(Show Context)
Citation Context ...rom another neighborhood of strings, these neighborhoods are just as isolated as two subpopulations on separate islands. This kind of separation is referred to as isolation by distance (Wright, 1932� =-=Muhlenbein, 1991-=-� Gorges-Schleuter, 1991). Of course, neighbors that are only 4 or 5 moves away have a greater potential for interaction. After the rst random population is evaluated, the pattern of strings over the ... |

95 | Fundamental principles of deception in genetic search,” in Foundations of Genetic Algorithms 1 - Whitley - 1991 |

92 | Real-Coded Genetic Algorithms and Interval Schemata - Eshelman, Schaffer - 1992 |

92 |
Genitor: a different genetic algorithm
- Whitley, Kauth
- 1988
(Show Context)
Citation Context ...or evolutionary strategies also tend to differ from Holland-type crossover, allowing operations such as averaging parameters, for example, to create an offspring. 8.1. Genitor Genitor (Whitley, 1989; =-=Whitley and Kauth, 1988-=-) was the first of what Syswerda (1989) has termed 'steady state' genetic algorithms. The name 'steady state' is somewhat misleading, since these algorithms show more variance than canonical algorithm... |

83 |
Genetic Algorithms in Noisy Environments
- Grefenstette, Fitzpatrick
- 1988
(Show Context)
Citation Context ... used to optimize some form of control strategy. In such cases, the state space must be sampled in a limited fashion and the resulting evaluation of control strategies is approximate and noisy (c.f., =-=Fitzpatrick and Grefenstette, 1988-=-). The evaluation function must also be relatively fast. This is typically true for any optimization method, but it may particularly pose an issue for genetic algorithms. Since a genetic algorithm wor... |

82 |
A Study of Reproduction in Generational and Steady-State Genetic Algorithms
- Syswerda
- 1990
(Show Context)
Citation Context ...dy state" genetic algorithms. The name \steady state" is somewhat misleading, since these algorithms show more variance than canonical genetic algorithms in the terms of hyperplane sampling behavior (=-=Syswerda, 1991-=-) and are therefore more susceptible to sample error and genetic drift. The advantage is that the best points found in the search are maintained in the population. This results in a more aggressive se... |

79 | Evolutionsstrategien und numerische Optimierung - Schwefel - 1975 |

76 |
Punctuated equilibria in genetic search
- Vose, E
(Show Context)
Citation Context ...e number of schemata available for processing fails to consider the dynamic behaviour of the genetic algorithm. As discussed later in this tutorial, dynamic models of the genetic algorithm now exist (=-=Vose and Liepins, 1991-=-; Whitley et al., 1992). There has not yet, however, been any real attempt to use these models to look at complex interactions between large numbers of hyperplane competitions. It is obvious in some v... |

75 |
Genitor ii: A distributed genetic algorithm
- Whitley, Starkweather
- 1990
(Show Context)
Citation Context ...l genetic algorithm, or Genitor, or CHC. Occasionally, perhaps every ve generations or so, the subpopulations would swap a few strings. This migration allows subpopulations to share genetic material (=-=Whitley and Starkweather, 1990-=-� Gorges-Schleuter, 1991� Tanese 1989� Starkweather et al., 1991.) Assume for a moment that one executes 16 separate genetic algorithms, each using a population of 100 strings without migration. In th... |

74 |
A Naturally Occurring Niche & Species Phenomenon: The Model and First Results
- Davidor
- 1991
(Show Context)
Citation Context ...ng is produced. and becomes the new resident at that processor. Several people have proposed this type of computational model (Manderick and Spiessens, 1989� Collins and Je erson, 1991� Hillis, 1990� =-=Davidor, 1991-=-). The common theme in cellular genetic algorithms is that selection and mating are typically restricted to a local neighborhood. There are no explicit islands in the model, but there is the potential... |

73 | Deception considered harmful
- Grefenstette
- 1993
(Show Context)
Citation Context ... were to try to use the schema theorem to predict the representation of a particular hyperplane over multiple generations, the resulting predictions would in many cases be useless or misleading (e.g. =-=Grefenstette, 1993-=-; Vose, personal communication, 1993). Second, the observed fitness of a hyperplane H at time t can change dramatically as the population concentrates its new samples in more specialized subpartitions... |

71 | A new interpretation of schema notation that overturns the binary encoding constraint - Antonisse - 1989 |

68 |
Adaptation
- Holland
- 1976
(Show Context)
Citation Context ... In the canonical genetic algorithm each member of this population will be a binary string of length L which corresponds to the problem encoding. Each string is sometimes referred to as a \genotype" (=-=Holland, 1975-=-) or, alternatively, a\chromosome" (Scha er, 1987). In most cases the initial population is generated randomly. After creating an initial population, each string is then evaluated and assigned a tness... |

62 |
Optimization using distributed genetic algorithms
- Starkweather, Mathias, et al.
- 1991
(Show Context)
Citation Context ...ve generations or so, the subpopulations would swap a few strings. This migration allows subpopulations to share genetic material (Whitley and Starkweather, 1990� Gorges-Schleuter, 1991� Tanese 1989� =-=Starkweather et al., 1991-=-.) Assume for a moment that one executes 16 separate genetic algorithms, each using a population of 100 strings without migration. In this case, 16 independent searches occur. Each search will be some... |

57 |
A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented
- Goldberg
- 1990
(Show Context)
Citation Context ...uation of all the strings in the population. Fitness can also be assigned based on a string's rank in the population (Baker, 1985� Whitley, 1989) or by sampling methods, such as tournament selection (=-=Goldberg, 1990-=-). It is helpful to view the execution of the genetic algorithm as a two stage process. It starts with the current population. Selection is applied to the current population to create an intermediate ... |

55 | An Executable Model of a Simple Genetic Algorithm. Foundations of Genetic Algorithms -2
- Whitley
- 1993
(Show Context)
Citation Context ...lly, a ne grain massively parallel implementation that assumes one individual resides at each processor will be explored. It can be shown that the ne grain models are a subclass of cellular automata (=-=Whitley 1993-=-b). Therefore, while these algorithms have been referred to by anumber of somewhat awkward names (e.g., ne grain genetic algorithms, or massively parallel genetic algorithms) the name cellular genetic... |

52 | Improving Search in Genetic Algorithms - Booker - 1987 |

49 |
Cellular genetic algorithms
- Whitley
- 1993
(Show Context)
Citation Context ...lly, a ne grain massively parallel implementation that assumes one individual resides at each processor will be explored. It can be shown that the ne grain models are a subclass of cellular automata (=-=Whitley 1993-=-b). Therefore, while these algorithms have been referred to by anumber of somewhat awkward names (e.g., ne grain genetic algorithms, or massively parallel genetic algorithms) the name cellular genetic... |

44 |
Explicit parallelism of genetic algorithms having population structures
- Gorges-Schleuter
- 1991
(Show Context)
Citation Context ... or CHC. Occasionally, perhaps every ve generations or so, the subpopulations would swap a few strings. This migration allows subpopulations to share genetic material (Whitley and Starkweather, 1990� =-=Gorges-Schleuter, 1991-=-� Tanese 1989� Starkweather et al., 1991.) Assume for a moment that one executes 16 separate genetic algorithms, each using a population of 100 strings without migration. In this case, 16 independent ... |

37 | Genetic algorithms�data structures �evolution programs - Michalewicz |

35 | An analysis of multi point crossover - Spears, Jong - 1991 |

34 | Arti cial Intelligence Through Simulated Evolution - Fogel, Owens, et al. - 1966 |

28 | C.L.: An analysis of a reordering operator on a GA-hard problem - Goldberg, Bridges - 1990 |

26 | An analysis of reproduction and crossover in a binary-coded genetic Algorithm - Bridges, Goldberg - 1987 |

24 | GENITOR: a Di erent Genetic Algorithm - Whitley, Kauth - 1988 |

23 |
The genitor algorithm and selective pressure
- Whitley
- 1989
(Show Context)
Citation Context ... is the evaluation associated with string i and f is the average evaluation of all the strings in the population. Fitness can also be assigned based on a string's rank in the population (Baker, 1985� =-=Whitley, 1989-=-) or by sampling methods, such as tournament selection (Goldberg, 1990). It is helpful to view the execution of the genetic algorithm as a two stage process. It starts with the current population. Sel... |