A Genetic Algorithm for Function Optimization : AMatlab

By ImplementationChristopher R. HouckNorth. 1996

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Citations in the corpus

Genetic CNN (2017, positive): “It is commonly used to generate high-quality solutions to optimization and search problems [14][30][2][4] by performing bio-inspired operators such as mutation, crossover and selection.”
Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior (2009, methodology): “, were set to be default as recommended in [40].”
Predator–prey cycles in an aquatic microcosm: testing hypotheses of mechanism (2002, methodology): “The first step implements a genetic algorithm to minimize the objective function ( Houck, Joines & Kay 1995 ).”
Comparison of two optimized machine learning models for predicting displacement of rainfall-induced landslide: A case study in Sichuan Province, China (2017, methodology): “This algorithm can be implemented in MATLAB with a combination of the GAOT toolbox (Houck et al., 1995) and LSSVM toolbox (Suykens et al., 2002).”
Taguchi method for solving the economic dispatch problem with nonsmooth cost functions (2005, methodology): “GA solutions are obtained with the help of a genetic algorithm optimization toolbox [ 7 ], the population size is chosen as 100, the crossover rate is 0.95, and the mutation rate is 0.01.”
Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension (2013, background): “This process is repeated until the performance stops progressing or when the maximum number of iterations has been reached; this evolutionary process therefore selects the most adapted set of predictors with respect to the given performance (15, 25, 26).”
GPLAB A Genetic Programming Toolbox for MATLAB (2004, background): “Until recently, there were a number of public domain genetic algorithm toolboxes for MATLAB [3, 5], but none specific for genetic programming.”
Design Considerations of a Direct Drive Brushless Machine With Concentrated Windings (2008, background): “A GA [5], [6] searches the solution space of a function using simulated evolution, where x is often referred to as a chromosome.”
A global multilevel coordinate search procedure for estimating the unsaturated soil hydraulic properties (2002, methodology): “We used the GA implemented in MATLAB by Houk et al. [1995], parameterized with its default values.”
Rayleigh wave dispersion curve inversion via genetic algorithms and Marginal Posterior Probability Density estimation (2007, methodology): “We tested different selection, crossover and mutation functions as well as parameter values but, as observed also by Sen and Stoffa (1992), we noticed that nonextreme variations of such functions and parameters scarcely influence the performances of the algorithms and we eventually adopted the value”