Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications

By Wolfgang Banzhaf, F. Francone, Robert E. Keller, P. Nordin. 1998

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Citations in the corpus

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).”
Evolutionary computation: a unified approach (2018, background): “Since then the GP community has grown significantly, both in size and diversity, to the point that there are are variety of books and specialty conferences in this area (see, for example, Banzhaf et al. (1998) or Langdon and Poli (2001)).”
Global Optimization Algorithms -- Theory and Application (2009, background): “Yet, recombination in Standard Genetic Programming can also have a very destructive effect on the individual fitness [170, 749, 673].”
RECIPE: A Grammar-Based Framework for Automatically Evolving Classification Pipelines (2017, background): “When genetic programming was first framed within the context of machine learning, its main idea was to generate complete programs following a “learning from examples” approach [1].”
Genetic Programming with a Genetic Algorithm for Feature Construction and Selection (2005, methodology): “In this paper we examine the use of Genetic Programming (GP) [4, 14] and a Genetic Algorithm (GA) [10] to improve the performance of, initially, C4.”
Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing (2013, unknown): “Genetic programming is an automated computational discovery tool that is inspired by Darwinian evolution by natural selection (Koza 1992; Banzhaf et al. 1998).”
Mathematical discoveries from program search with large language models (2023, methodology): “Genetic programming is a subfield of computer science concerned with automatically generating or discovering computer programs using evolutionary methods 15,72,73 and is used for symbolic regression applications 74,75 and discovery of optimization algorithms 76 among others.”
Meta optimization: improving compiler heuristics with machine learning (2003, methodology): “For details on the mutation operators we implemented, see [2].”
Advances in Computational Intelligence (2003, unknown): “There are GP algorithms that use other kinds of individual representation [Banzhaf et al. 1998], but our discussion here is limited to tree-based representations, which are by far the most used ones, even though no representation is universally best for all application do-”
A Survey of Evolutionary Algorithms for Decision-Tree Induction (2012, methodology): “The penalty factor is used to alleviate the bloat problem [96].”
AlphaEvolve: A coding agent for scientific and algorithmic discovery (2025, unknown): “AlphaEvolve extends a long tradition of research on evolutionary or genetic programming [54], where one repeatedly uses a set of mutation and crossover operators to evolve a pool of programs [5, 51].”