Genetic Programming: An Introduction

By Wolfgang Banzhaf. 1997

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In Evolutionary Computation

Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, positive): “(2016a) and is based on genetic programming (Koza, 1992; Banzhaf et al., 1997).”
Survey on Automated Machine Learning (2019, positive): “(2016a) and is based on genetic programming (Banzhaf et al., 1997).”
Automating Biomedical Data Science Through Tree-Based Pipeline Optimization (2016, positive): “We use a well-known evolutionary computation technique called genetic programming [5] to automatically construct a series of data transformations and machine learning models that act on the data set with the goal of maximizing classiﬁcation accuracy.”
Optimal Ordered Problem Solver (2002, neutral): “Relatively recent general attempts include program evolvers such as Adate (Olsson, 1995) and simpler heuristics such as Genetic Programming (GP) (Cramer, 1985, Banzhaf et al., 1998).”
TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning (2016, positive): “To automatically generate and optimize these tree-based pipelines, we use a genetic programming (GP) algorithm [1] as implemented in the Python package DEAP [7].”
Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification (2018, background): “] is a class of population-based meta-heuristic optimization paradigms inspired by the biological evolution. Typical evolutionary algorithms include genetic algorithms (GAs) [23], genetic programming [24], evolutionary strategy [25], etc., among which GAs are the most popular one mainly because of t”
From evolutionary computation to the evolution of things (2015, unknown): “Today, the family of EAs includes historical members: genetic algorithms, evolution strategies, evolutionary programming, and genetic programming; and younger siblings, such as differential evolution and particle swarm optimization 17,18,19,20,21,22,23,24,25 .”
Redundancy and computational efficiency in Cartesian genetic programming (2006, background): “In register machine code GP [ 4 ], [39], this can have the effect of automatically suppressing the growth of implicit introns.”
Indoor localization without the pain (2010, unknown): “The other alternatives are search techniques such as simulated annealing [15] or genetic algorithms [6] (GAs).”
Genetic Programming and Autoconstructive Evolution with the Push Programming Language (2002, methodology): “PushGP uses tournament selection [ 7 ] and reasonably standard genetic operators although the flexibility of the Push syntax invites the development of novel operators as well.”
Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements (2003, unknown): “Similar drawbacks hold for Lenat’s human-assisted, non-autonomous, self-modifying learner [21], our Meta-Genetic Programming [29] extending Cramer’s Genetic Programming [8, 1], our metalearning economies [29] extending Holland’s machine learning economies [14], and gradient-based metalearners for co”
A Genetic Algorithm-Based Approach for Building Accurate Decision Trees (2003, methodology): “GP has been applied to a wide variety of problems in science, computer science, and engineering (see Chapter 12 in Banzhaf et al. (1998) for a very nice discussion of GP applications in these areas).”
How the body shapes the way we think - a new view on intelligence (2006, unknown): “In genetic programming (Banzhaf et al., 1998), the genotype is a tree structure which represents a computer program (a string cannot be used for the representation because if one bit is not correct, the program does not run and has a fitness of zero, and will thus not contribute to the evolutionary ”