J.J. Grefenstette, "Optimization of control parameters for genetic algorithms", IEEE Trans. Systems, Man, and Cybernetics, Vol. 16-1, pp. 122-128, 1986 Home/Search Document Not in Database Summary Related Articles Check |
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Operator Learning for a Problem Class in a Distributed.. - Jelasity, Preuss, Eiben (2002) (Correct)
....of adaptation or learning during the optimization process. The generated knowledge is normally not considered scientifically important and is discarded after the completion of the algorithm. The large field of self calibrating algorithms (which include the meta GA approach) belongs to this class [7, 3]. The main idea is that the values of different algorithm parameters are automatically tuned on the fly to achieve optimal performance while solving a problem. Another successful idea is building probabilistic representations of the fitness landscape based on the solutions evaluated during the ....
J. J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Trans. Systems, Man, Cybern., 16(1):122--128, 1986.
Evolutionary Computation: Comments on the History and.. - Bäck, Hammel, Schwefel (1997) (Correct)
....evolutionary programming , and evolution strategies. Genetic algorithms, introduced by Holland [6] 11] 12] and subsequently studied by De Jong [13] 14] 15] 16] Goldberg [17] 18] 19] 20] 21] and others such as Davis [22] Eshelman [23] 24] Forrest [25] Grefenstette [26], 27] 28] 29] Koza [30] 31] Mitchell [32] Riolo [33] 34] Schaffer [35] 36] 37] to name only a few, have been originally proposed as a general model of adaptive processes, but by far the largest application of the techniques is in the domain of optimization [15] 16] Since this ....
J. J. Grefenstette, "Optimization of control parameters for genetic algorithms," IEEE Transactions on Systems, Man and Cybernetics, vol. SMC--16, no. 1, pp. 122--128, 1986.
Population Size and Sampling Complexity in Genetic Algorithms - Gao (2003) (Correct)
.... in [Bertoni and Dorigo, 1993] In the practice of designing e#cient genetic algorithms, there has been strong empirical evidence showing that population size is one of the most important parameters that plays a significant role in the performance of the genetic algorithms [Leung et al. 1997, Grefenstette, 1986, Eiben et al. 1999] There also has been some theoretical work dealing with the problem of bounding the population size. For example, in [Goldberg et al. 1992, Harik et al. 1997] population size equations were derived by considering a specific set of competing schemata under some assumptions ....
J. J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Trans. on Systems, Man, and Cybernetics, 16(1):122--128, 1986.
MERBIS - A Multi-Objective Evolutionary Rule Base Induction.. - Setzkorn, Paton (2003) (Correct)
....(MP) 0.001, 0.01, 0.3 Two Individuals Genetic Operator Intensity Probability (IPGO2) GO21 or GO22 : Crossover Probability (CP) 0.3, 0.6, 0.95 Table 2. The parameter values used for the experiments. The choice of parameters follows from previous work of De Jong [30] Grefenstette [22] and Laumanns et al. 37] For example, De Jong recommended a crossover probability (parameter CP ) of 0:6 and a mutation probability (parameter MP ) of 0:001. Grefenstette, on the other hand, suggested a CP of 0:95 and a MP of 0:01. Laumanns et al. recommended high mutation probabilities together ....
J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, SMC16, 1:122--128, 1986.
Parallel Genetic Algorithm with Parameter Adaptation - Tongchim, Chongstitvatana (2002) (2 citations) (Correct)
....study showed that the following parameters yielded a good performance: population size 50 100, onepoint crossover probability of 0.6 and bit mutation probability of 0.001. These parameter values have been widely used by many researchers and accepted as a standard parameter setting. Grefenstette [7] investigated the use of meta level GA to select feasible parameter values. The method was designed as two levels of GA. The higher level GA maintained a population of parameter sets. The lower level GA used the parameter sets from the higher level GA to solve the problems. The observed ....
....sets simultaneously. Another di#erence is that Pham uses static parameter sets, whereas our method dynamically adjusts parameter sets according to the observed performance. The proposed method can be viewed as meta level GA. However, this method mainly di#ers from the work done by Grefenstette [7] that the parameters are adjusted during the run of the algorithm, whereas the method of Grefenstette finds the parameters before the run of the algorithm. 0: initialize the population, P 1: while generation max generation 2: evaluate P 3: apply genetic operators determined by the first ....
J. J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 16(1):122--128, Jan/Feb 1986.
Applied Cloning Techniques for a Genetic Algorithm Used in .. - Trinh, Holifield, Wu (Correct)
....a new applied cloning strategy to evolve optimum circuit designs. Cloning, in effect , will keep the population constraint intact while allowing for a larger search medium. Motivation Many researchers have explored natural biological principles and applied them to the field of genetic algorithms [5]. Because of the limited amount of natural biological principles to apply to genetic algorithms, this paper takes into account an artificial biological principle, namely cloning, and implements a genetic algorithm based on it. This method is taken into account because it allows deeper research ....
Grefenstette, J. J., "Optimization of Control Parameters for Genetic Algorithms", IEEE Transactions on Systems, Man, and Cybernetics , Vol 16, No. 1, pp. 122-128, 1986.
Hybrid Computational Intelligence Schemes in Complex.. - Tsakonas, Dounias (2002) (Correct)
....that includes the attributes of both sub components, such as fuzzy inference and genetic based self training. Genetic algorithms controlled by fuzzy logic. Applications of genetic algorithms controlled by fuzzy logic can be found in a series of publications [57] 58] 59] 60] 61] 62] [63] and [64] In [65] the authors use fuzzy coding for genetic optimization. This approach enables to establish a relevant level of information granularity and to provide with some search guidance. In [66] fuzzy logic controllers are used for the adaptation of genetic algorithms parameters. In ....
Grefenstette J., Optimization of control parameters for genetic algorithms, IEEE Trans. Syst. Man Cybernet. SMC-16, 122-128, 1986
Models for Evolutionary Algorithms and Their Applications in.. - Ursem (Correct)
....(2.3) where u is uniformly distributed according to U(0, 1) If the position t # of the next bit flip is not in the current genome then the first bit flipped in the next mutated genome should be the (t # L) th bit. Empirical studies have suggested values for p m [0.001, 0. 01] e.g. 35] and [62]) Bremermann [23] and later Back [8] showed that the value p m = 1 L is optimal for simple sphere problems. For this reason, 1 L is usually used as a lower bound on p m . N point and uniform crossover A widely used crossover operator for binary (and also real encoding) is the n point ....
....one bit per genome on average. Furthermore, p m = 1 L has been proven optimal for the sphere problem [23] 8] Over the years several constants for p m have been suggested. De Jong found p m = 0.001 to be appropriate on a number of simple benchmark problems [35] Grefenstette suggested p m = 0. 01 [62], and Scha#er et al. recommend a range of p m [0.005, 0.01] 120] For real value encoding, the probability of mutation is usually p m [0.6, 0.9] and probability of crossover p c [0.7, 1.0] In tournament selection, a tournament size of two often gives the best results. Setting it much ....
Grefenstette, J. J. (1986). Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 16(1):122--128.
A Distributed Resource and Network Partitioning Architecture .. - Marc De Leenheer (Correct)
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J.J. Grefenstette, "Optimization of control parameters for genetic algorithms", IEEE Trans. Systems, Man, and Cybernetics, Vol. 16-1, pp. 122-128, 1986
An Adaptive Genetic Algorithm for Permutation Based - Optimization Problems Koh (Correct)
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Grefenstette, J. J. (1986), "Optimization of control parameters for genetic algorithms," in IEEE Trans. On Systems, Man, and Cybernetics.
Inducing Safer Oblique Trees without Costs - Vadera (2005) (Correct)
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GREFENSTETTE, J. (1986) Optimization of control parameters for genetic algorithms, IEEE Transactions on Systems, Man, and Cybernetics, 16, 122--128.
Predictive Models for the Breeder Genetic Algorithm - .. - Mühlenbein.. (1993) (1 citation) (Correct)
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Grefenstette, J. J. (1986). Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 16, 122--128.
A Distributed Resource and Network Partitioning.. - Volckaert.. (2005) (Correct)
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J.J. Grefenstette, "Optimization of control parameters for genetic algorithms", IEEE Trans. Systems, Man, and Cybernetics, Vol. 16-1, pp. 122-128, 1986
Multiresolution Genetic Clustering Algorithm for Texture.. - Chang-Tsun Li Randy (2003) (Correct)
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J.J. Grefenstette, Optimization of control parameters for genetic algorithms, IEEE Transactions on Systems, Man, and Cybernetics 16 (1) (1986) 122 -- 128.
Unsupervised and Supervised Data Classification.. - Bagirov, Rubinov, .. (Correct)
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J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems Man and Cybernetics, 1:122--128, Jan/Feb 1986.
Evolving Objects: a general purpose evolutionary.. - Keijzer, Merelo.. (2001) (6 citations) (Correct)
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J.J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, SMC-16, 1986.
A Hybrid Evolutionary Algorithm for Gait Generation of.. - Dragos Golubovic And (2002) (1 citation) (Correct)
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J.J. Grefenstette, "Optimization of Control Parameters for Genetic Algorithms", IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-16, No. 1, January/Febuary, 1986.
Generational Parallel Varying Mutation GAs and their Applications - Duran (2003) (Correct)
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J. J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, (16):122--128, 1986.
Boosting Stochastic Problem Solvers through Online Self-Analysis .. - Cicirello (2003) (Correct)
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J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 16(1):122--128, Jan-Feb 1986.
Parameter Optimisation of an Evolutionary Algorithm for.. - Golubovic, Hu (2003) (Correct)
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J. Grefenstette, "Optimization of Control Parameters for Genetic Algorithms", IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-16, No. 1, January/Febuary, 1986.
Stochastic Algorithms for Buffer Allocation in Reliable.. - Spinellis, Papadopoulos (1999) (Correct)
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J. J. Grefenstette. Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 16(1):122--128, 1986.
Intelligent Parameter Adaptation for Chemical Processes - Sozio (1999) (Correct)
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Grefenstette, J.J., "Optimization of control parameters of genetic algorithms," IEEE Transactions on Systems, Man, and Cybernetics. 16, 1986.
Automatic Discovery of Self-Replicating Structures in Cellular .. - Lohn, Reggia (1997) (3 citations) (Correct)
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J. Grefenstette, "Optimization of Control Parameters for Genetic Algorithms," IEEE Transactions on Systems, Man, and Cybernetics, vol. 16, No. 1, pp. 122--128, 1986.
Parameter Control in Evolutionary Algorithms - Eiben, Hinterding, Michalewicz (1999) (21 citations) (Correct)
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J.J. Grefenstette. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 16(1):122--128, 1986.
Exploring the Design Space of Artificial Self-Replicating.. - Lohn, al. (2000) (Correct)
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Grefenstette, J. (1986), "Optimization of Control Parameters for Genetic Algorithms," IEEE Transactions on Systems, Man, and Cybernetics, vol. 16, No. 1, pp. 122--128.
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