Genetic Programming: On the Programming of Computers by Means of Natural Selection

By John R. Koza. 1992

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, background): “Perhaps they could, using genetic programming methods (Koza, 1992) or other structuresearch algorithms (Yamins et al.”
Deep learning in neural networks: An overview (2014, background): “…brought simple NNs trained by SL (e.g., Narendra & Thathatchar, 1974; Rosenblatt, 1958, 1962; Widrow & Hoff, 1962) and UL (e.g., Grossberg, 1969; Kohonen, 1972; von der Malsburg, 1973; Willshaw & von der Malsburg, 1976), as well as closely related associative memories (e.g., Hopfield, 1982;…”
Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers (2016, background): “Instead, drawing ideas from genetic programming (GP) [11, 15], we perform stochastic manipulations and then evaluate the generated variants to select promising ones.”
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (2020, background): “F is then evolved, making this an example of genetic programming [36].”
Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “The first method to build flexible ML pipelines automatically was introduced by Olson and Moore (2016) and is based on genetic programming (Koza, 1992; Banzhaf, Nordin, Keller, & Francone, 1997).”
Survey on Automated Machine Learning (2019, methodology): “these operators is displayed in Figure 4. The rst method to build exible ML pipelines automatically was introduced by Olson and Moore (2016); Olson et al. (2016a) and is based on genetic programming (Koza, 1992; Banzhaf et al., 1997). Genetic programming has been used for automatic program code 0DFK”
Discovering governing equations from data by sparse identification of nonlinear dynamical systems (2015, background): “Typically, the function is determined using genetic programming, which is an evolutionary algorithm that builds and tests candidate functions out of simple building blocks [22].”
Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “This idea led to the belief that intelligent computer programs could emerge with little human intervention by means of evolution in-silico (Holland and Reitman, 1977; Koza, 1992; Fogel, 2006).”
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities (2018, background): “One such algorithm is GenProg [45], which applies digital evolution to evolve code (called genetic programming [68]).”
Intraday Technical Trading in the Foreign Exchange Market (2003, unknown): “These procedures were developed by Holland (1975) and extended by Koza (1992).”
Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, background): “Rowland (2003) gives an excellent example of what may be required, in the context of genetic programming, a technique (e.g. (Koza, 1992; Langdon, 1998; Koza et al., 2003)) that we favour since it gives easily interpretable rules that relate to actual metabolites rather than to latent variables and…”
Gene Expression Programming: A New Adaptive Algorithm for Solving Problems (2001, methodology): “The comparison of F z values obtained by GEP and GP [6] for this problem (Table 3, column 1) shows that GEP surpasses GP in 374 times, more than two orders of magnitude.”
Machine learning in geosciences and remote sensing (2016, background): “Among the main subsets of ML, applications of genetic programming (GP) (Koza, 1992) in the geoscience and remote sensing domain are very new and restricted to a few areas.”
Artificial Evolution in the Physical World (1997, background): “a genetic algorithm (GA) [7, 4, 5], evolutionary programming [3], evolution strategies [13] or genetic programming [8, 9]) until satisfactory performance is achieved.”
Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects (2014, methodology): “NNs are considered a relatively new technology in Finance, but with high potential and an increasing number of applications (Lam (2004) and Bahrammirzaee (2010)).”
Evolutionary computation and structural design: A survey of the state-of-the-art (2005, background): “On the other hand, the fourth major EA developed more recently, Genetic Programming (GP) [14], has been used to evolve actual computer programs to solve a number of computational tasks [10].”
Spontaneous evolution of modularity and network motifs. (2005, background): “These include enforcement of regular structures by using tree architectures (1) or generative grammars (21).”