On the practical genetic algorithms

By Chang Wook Ahn, Sanghoun Oh, Rudrapatna S. Ramakrishna. Annual Conference on Genetic and Evolutionary Computation, 2005

1,986 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/909c832685cd66a835857478f586225d49c52261

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Citations in the corpus

Genetic Algorithm Optimization for Microstrip Patch Antenna Miniaturization (2016, positive): “The concept of the GA, ﬁrst formalized by Holland and extended to functional optimization by De Jong, involves the use of optimization search strategies patterned after the Darwinian notion of natural selection and evolution [12].”
Black hole: A new heuristic optimization approach for data clustering (2013, background): “For instance genetic algorithms (GAs) are search and optimization techniques that evolve a population of candidate solutions to a given problem, using natural genetic variation and natural selection operators [41].”
Cuckoo Optimization Algorithm (2011, background): “[1], one 10imensional Rastrigin function and a real case study.”
Soil parameter identification using a genetic algorithm (2008, methodology): “In the engineering literature, the genetic algorithm is well known to be able to solve complex optimization problems [15–21].”
A fast and general upper-bound limit analysis approach for out-of-plane loaded masonry walls (2017, methodology): “In this paper, a weighted cost selection with assigned probabilities is used [46].”
Knot selection for least-squares and penalized splines (2013, methodology): “This results in a continuous genetic algorithm in the sense described by Haupt and Haupt [32] and references therein.”
Design of shell-and-tube heat exchangers using multiobjective optimization (2013, background): “A number of earlier optimization studies using GAs only considered a single objective.”
Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy (2014, background): “The input consists of variables: the process or function is known as the cost function, objective function, or fitness function; and the output is the cost or fitness (Haupt & Haupt, 2004).”
Global Optimization Algorithms -- Theory and Application (2009, methodology): “Goldberg [93]: Genetic Algorithms in Search, Optimization and Machine Learning Mitchell [49]: An Introduction to Genetic Algorithms Davis [582]: Handbook of Genetic Algorithms Haupt and Haupt [583]: Practical Genetic Algorithms Gen and Cheng [584]: Genetic Algorithms and Engineering Design Chambers ”
An hybrid real genetic algorithm to detect structural damage using modal properties (2011, background): “In addition RCGA do not have a limited precision as do BCGA; this allows a representation to themachine precision [35].”
Vibration suppression of structures using tuned mass damper technology: A state-of-the-art review (2021, methodology): “As the introduction of GA, SA, and numerical searching method is out of the scope of this study, for more detailed information about GA, SA, and numerical searching algorithm, one can consult the books and article by Haupt and Haupt (2004) and Aarts and Korst (1989).”
Optimal power flow: a bibliographic survey II (2012, background): “Genetic Algorithms (GAs) are evolutionary search algorithms based on the mechanics of natural genetics [69, 154].”