A Field Guide to Genetic Programming

By Riccardo Poli, William B. Langdon, Nicholas Freitag McPhee. 2008

2,207 citations recorded by the source corpus.

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Citations in the corpus

Survey on Automated Machine Learning (2019, methodology): “Genetic programming has been used for automatic program code generation for a long time (Poli et al., 2008).”
A Roadmap Towards Machine Intelligence (2015, background): “Genetic programming (Poli et al., 2008) also focuses on the reuse of previously found sub-solutions, speeding up the search procedure in this way.”
Post-compiler software optimization for reducing energy (2014, methodology): “If a crossover is to be performed, two high-ﬁtness parents are chosen from the population via tournament selection [46, § 2.3] AddTo ( Pop, (cid:104) p (cid:48) , Fitness ( Run ( p (cid:48) )) (cid:105) ) 14: EvictFrom ( Pop, Tournament ( Pop, TournamentSize, − )) single high-ﬁtness optimization is…”
Semantically-based crossover in genetic programming: application to real-valued symbolic regression (2011, background): “Genetic programming (GP) [40, 49 , 36]) researchers have only recently paid much attention to semantic information, which has resulted in a dramatic increase in the number of realated publications (e.g.”
Feature Learning for Image Classification Via Multiobjective Genetic Programming (2014, unknown): “2.”
From Black-Box to Transparent Computational Intelligence Models: A Pharmaceutical Case Study (2015, background): “GP is a bioinspired algorithm based on evolution principles to solve complex problems [11].”
A Multi-Facet Survey on Memetic Computation (2011, background): “Typical population-based search frameworks including genetic algorithm (GA) [8], evolution strategy (ES) [9], evolutionary programming [10], genetic programming [11], differential evolution (DE) [12], estimation of distribution algorithm (EDA) [13], ant colony optimization (ACO) [14], particle swarm”
A survey on optimization metaheuristics (2013, methodology): “Other forms of crossover have been defined and used, such as one-point crossover , context-preserv ing crossover , size-fair crossover and uniform crossover [209].”
Applications of Evolutionary Computation (2016, methodology): “For example, this has been achieved for the dominated hypervolume indicator in case of two objectives [9].”
GPTIPS: An Open Source Genetic Programming Toolbox For Multigene Symbolic Regression (2010, background): “If this is not the case then an excellent, free to download introduction and review of the literature is provided by [7].”
On Using Surrogates with Genetic Programming (2015, methodology): “…by guest on 22 M ay 2021 In the next step (step 5), conventional GP operators (see Koza, 1992; Poli et al., 2008) for selection, mutation, and crossover are used to produce a new, intermediate population Pimd taking into account all individuals…”
EDEN: Evolutionary deep networks for efficient machine learning (2017, methodology): “The initial population generation method used in this study was inspired by the ramped-half-and-half method proposed by Koza [20] which enables the creation of candidate solutions of various sizes.”
Applications of Probabilistic Programming (Master's thesis, 2015) (2016, background): “For a recent introduction into the field of genetic programming, see (Poli et al., 2008).”