Genetic Algorithms in Search

By D. E. Goldberg. 1989

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “RL NNs can also be evolved through Evolutionary Algorithms (EAs) (Fogel, Owens, & Walsh, 1966; Goldberg, 1989; Holland, 1975; Rechenberg, 1971; Schwefel, 1974) in a series of trials.”
Integrated facilities design using a contour distance metric (2001, unknown): “…of Figure 1 would be represented with the following encoding: G A F H B E K C L M I J D H K I where the last three characters indicate bay breaks after departments H, K and I. Crossover is accomplished through a variant of uniform crossover (Goldberg 1989), where two parents create one offspring.”
Global Minimum-Jerk Trajectory Planning of Space Manipulator (2006, background): “The global search characteristics of GA can be found from reference [13], we will not verify it here.”
Evolutionary Computation (2017, unknown): “Parts of this section are adapted from Chapter 1 of Goldberg (1989).”
An indirect Genetic Algorithm for a nurse-scheduling problem (2004, background): “and mutation operators as explained for instance in Goldberg [ 20 ].”
Genetic learning of fuzzy cognitive maps (2005, methodology): “Their origin and principles were inspired by natural genetics [10].”
Parameter sensitivity analysis for different complexity land surface models using multicriteria methods (2006, methodology): “By introducing the Pareto ranking concept [ Goldberg, 1989 ] as the technique for selecting the discriminatory threshold for behavioral and nonbehavioral parameter sets, MOGSA takes into account the multicriteria nature of typical sensitivity problems of environmental models.”
A Privacy-Preserving Index for Range Queries (2004, background): “Such problems are combinatorial in nature and the most popular solution techniques seem to revolve around the Genetic Algorithm (GA) framework [ 20 ], [21].”
A genetic algorithmic approach for optimization of surface roughness prediction model (2002, background): “The three stages are (a) Reproduction (b) Cross-over (c) Mutation [8,10].”
Hybrid Taguchi-genetic algorithm for global numerical optimization (2004, background): “On the other hand, among the existing optimization algorithms, the genetic algorithm (GA) has received considerable attention regarding its potential as a novel optimization technique for complex problems and has been successfully applied in various areas [7], [8].”
Gene Expression Programming: A New Adaptive Algorithm for Solving Problems (2001, methodology): “In GEP, individuals are selected according to fitness by roulette-wheel sampling [ 5 ].”
Metaheuristics in Combinatorial Optimization (2005, background): “For a broader view on metaheuristics, one may refer to the following books, monographies and edited volumes (Aarts and Lenstra, 1997; Corne, Dorigo, and Glover, 1999; Glover and Kochenberger, 2003; Glover and Laguna, 1997; Goldberg, 1989; Holland, 1975; Laporte and Osman, 1996; Michalewicz, 1996; ”
A connectionist central pattern generator for the aquatic and terrestrial gaits of a simulated salamander (2001, methodology): “…is used for instantiating theparameters of a network whose architecture is de nedby hand.4.1 Genetic AlgorithmThe algorithm used is a genetic algorithm (Holland,1975; Goldberg, 1989) using a real number encoding To appear in Biological Cybernetics 5instead of the traditional binary encoding.”
An adaptive reanalysis method for genetic algorithm with application to fast truss optimization (2010, background): “Genetic algorithm (GA) is stochastic, inspired by natural evolution, hereditary and survival of the fittest [ 13 ].”