Foundations of Genetic Programming

By William B. Langdon, Riccardo Poli. Springer Berlin Heidelberg, 2002

877 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7ff03b371acc40f5ca38c19876ebec499a7959d9

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In Artificial Intelligence

Citations in the corpus

Diversity in genetic programming: an analysis of measures and correlation with fitness (2004, background): “The parity problem has also been investigated in detail in [30].”
Theoretical advances in artificial immune systems (2008, background): “There are now many significant theoretical results on evolutionary algorithms (both in terms of schema [89], and using the theory of Markov chains [71]).”
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.”
Evolutionary computation: a unified approach (2018, background): “Rather, it provides a ”litmus test” for particular design choices by emphasizing the importance of balancing these two forces (see, for example, Langdon and Poli (2001)). In addition, it helps explain how EAs with strong selection pressure and aggressive reproductive variation can exhibit a level of”
Music-Inspired Harmony Search Algorithm (2009, methodology): “They are also referred to as exploration and exploitation [6].”
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model (2024, background): “Genetic Programming (GP) is commonly employed for AHD due to its efficacy across diverse domains (Langdon & Poli, 2013; Zhang et al., 2023).”
Global Optimization Algorithms -- Theory and Application (2009, neutral): “Langdon and Poli [51](35) explain that fitness landscapes can be imagined as a view on a countryside from far above.”
Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming (2009, methodology): “Inspired by principles of natural selection, symbolic regression via GP sets up an artificial evolution of models, optimizing some of them, evaluating how well they fit the observed data, and then using this information to decide which models to use as parents for the next generation [8], [ 10 ]–[12”
Semantically-based crossover in genetic programming: application to real-valued symbolic regression (2011, background): “Genetic Programming (GP) [36, 40, 49]) researchers have only recently paid much attention to semantic information, which has resulted in a dramatic increase in the number of realated publications (e.”