Genetic algorithms: principles of natural selection applied to computation.

By Stephanie Forrest. 1993

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Citations in the corpus

Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers (2016, positive): “Instead, drawing ideas from genetic programming (GP) [11, 15], we perform stochastic manipulations and then evaluate the generated variants to select promising ones.”
Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, unknown): “In nature, the genetic information is defined in a quaternary code, based on the four nucleotides Adenine, Cytosine, Guanine and Thymine, stringed together in a DNA sequence, which forms the basis of the genetic code [36].”
Evolutionary optimization as applied to inverse scattering problems (2009, unknown): “GAs basic principles were first introduced by Holland in 1975 [75] and extended to functional optimization by De Jong [47] with an immediate diffusion to real-world problems because of their effectiveness in dealing with complex functions [61, 69, 77] when compared to standard deterministic procedur”
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems (2008, unknown): “However, premature convergence on a local optimum can occur if the GA magnifies a small sampling error [63].”
Genetic algorithms in chemometrics and chemistry: a review (2001, background): “Computer Science and Statistics, 1997; 28: 545–550.”
Automatically finding patches using genetic programming (2009, unknown): “As mentioned earlier, GP is a stochastic search method based on principles of biological evolution [14, 19].”
Chemical-Reaction-Inspired Metaheuristic for Optimization (2010, background): “Among the most famous ones are simulated annealing (SA) [4], genetic algorithm (GA) [5]–[7], and ant colony optimization (ACO) [8], [9].”
Complete search in continuous global optimization and constraint satisfaction (2004, unknown): “…1993, Van Laarhoven and Aarts 1987), and deterministic variants such as threshold accepting (Dueck and Scheuer 1990); ensemble methods, including genetic algorithms (Holland 1973, Forrest 1993, Michalewicz 1996) and variants such as ant colony minimization (Dorigo, Maniezzo and Colorni 1991).”
A mixability theory for the role of sex in evolution (2008, background): “The hope had been that, starting from a random population of genotypes, genotypes will become fitter through the simulated evolution and eventually provide the best combination of parameters or global peak, even when the fitness landscape is too large to be scanned exhaustively (3, 4).”
Cryptic male choice: sperm allocation strategies when female quality varies (2002, unknown): “This procedure helps to maintain genetic variation under strong selection and is a standard selection procedure used in genetic algorithms (Forrest, 1993).”