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A Probabilistic Linear Genetic Programming with Stochastic Context-Free Grammar for solving Symbolic Regression problems

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Léo Françoso Dal Piccol Sotto, Vinícius Veloso de Melo

Neural and Evolutionary Computing · Submitted 3 Apr 2017

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Tests performed with the proposed probabilistic method, and with two hybrid approaches, on several symbolic regression benchmark problems show that the results are statistically better than the obtained by the traditional LGP.

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arXiv ID1704.00828 (version 1)
Pith IDVJ5IPOM5, Pith's permanent record of this paper and its review
CitationL\'eo Fran\c{c}oso Dal Piccol Sotto, Vin\'icius Veloso de Melo. "A Probabilistic Linear Genetic Programming with Stochastic Context-Free Grammar for solving Symbolic Regression problems." arXiv:1704.00828 (2017). https://pith.science/paper/VJ5IPOM5
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,902 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.06 · human participants 0.02 · controlled comparison 0.93 · randomized assignment 0.02 · states quantitative result 0.10 · compares with prior results 0.97 · mentions code or data availability 0.02.

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arXiv listing abstract v1. Results and the main claim are underlined; .

Traditional Linear Genetic Programming (LGP) algorithms are based only on the selection mechanism to guide the search. Genetic operators combine or mutate random portions of the individuals, without knowing if the result will lead to a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP) methods were proposed to overcome this issue through a probability model that captures the structure of the fit individuals and use it to sample new individuals. This work proposes the use of LGP with a Stochastic Context-Free Grammar (SCFG), that has a probability distribution that is updated according to selected individuals. We proposed a method for adapting the grammar into the linear representation of LGP. Tests performed with the proposed probabilistic method, and with two hybrid approaches, on several symbolic regression benchmark problems show that the results are statistically better than the obtained by the traditional LGP.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Traditional Linear Genetic Programming (LGP) algorithms are based only on the selection mechanism to guide the search. background 0.99 0.05 0.02 asserted 1.00
s1 Genetic operators combine or mutate random portions of the individuals, without knowing if the result will lead to a fitter individual. background 0.86 0.05 0.02 asserted 1.00
s2 Probabilistic Model Building Genetic Programming (PMB-GP) methods were proposed to overcome this issue through a probability model that captures the structure of the fit individuals and use it to sample new individuals. background 1.00 0.04 0.04 asserted 1.00
s3 This work proposes the use of LGP with a Stochastic Context-Free Grammar (SCFG), that has a probability distribution that is updated according to selected individuals. contribution 0.85 0.76 0.08 asserted 0.99
s4 We proposed a method for adapting the grammar into the linear representation of LGP. contribution 0.52 0.93 0.04 asserted 1.00
s5 Tests performed with the proposed probabilistic method, and with two hybrid approaches, on several symbolic regression benchmark problems show that the results are statistically better than the obtained by the traditional LGP. result 1.00 0.96 0.91 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.73.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Traditional Linear Genetic Programming (LGP) algorithms are based only on the selection mechanism to guide the search. Genetic operators combine or mutate random portions of the individuals, without knowing if the result will lead to a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP) methods were proposed to overcome this issue through a probability model that captures the structure of the fit individuals and use it to sample new individuals. This work proposes the use of LGP with a Stochastic Context-Free Grammar (SCFG), that has a probability distribution that is updated according to selected individuals. We proposed a method for adapting the grammar into the linear representation of LGP. Tests performed with the proposed probabilistic method, and with two hybrid approaches, on several symbolic regression benchmark problems show that the results are statistically better than the obtained by the traditional LGP.

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