• Corpus ID: 108285816

A Model-based Genetic Programming Approach for Symbolic Regression of Small Expressions

@article{Virgolin2019AMG,
  title={A Model-based Genetic Programming Approach for Symbolic Regression of Small Expressions},
  author={M. Virgolin and Tanja Alderliesten and Cees Witteveen and Peter A. N. Bosman},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.02050}
}
The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts randomly, GOMEA learns a model of interdependencies within the genotype, i.e., the linkage, to estimate what patterns to propagate. In this article, we study the role of Linkage Learning (LL) performed by GOMEA in Symbolic Regression (SR). We show that the non… 

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References

SHOWING 1-10 OF 59 REFERENCES
Improving genetic programming based symbolic regression using deterministic machine learning
TLDR
It is shown that a hybrid deterministic/GP-SR algorithm outperforms GP-SR alone and the state-of-the-art deterministic regression technique alone on a set of multivariate polynomial symbolic regression tasks as the system to be modeled becomes more multivariate.
Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning
TLDR
On a set of well-known benchmark problems, GP-GOMEA outperforms standard GP while being on par with more recently introduced, state-of-the-art EAs, and introduces Input-space Entropy-based Building-block Learning (IEBL), a novel approach to identifying and encapsulating relevant building blocks (subroutines) into new terminals and functions.
Multifactorial Genetic Programming for Symbolic Regression Problems
TLDR
This is the first attempt in the literature to conduct multitasking GP using a single population using a novel multifactorial GP algorithm which consists of a novel scalable chromosome encoding scheme which is capable of representing multiple solutions simultaneously.
Generalisation and domain adaptation in GP with gradient descent for symbolic regression
TLDR
The results suggest that the existing GPGD method applying gradient descent to all evolved program trees three times at every generation can perform very well on the training set itself, but cannot generalise well onthe unseen data set in the same domain and cannot be adapted to unseen data in an extended domain.
Multi-objective gene-pool optimal mixing evolutionary algorithms
TLDR
This work modifications the linkage learning procedure and the variation operator of GOMEAs to better suit the need of finding the whole Pareto-optimal front rather than a single best solution, and constructs a multi-objective GOMEA (MO-GOMEA).
An Analysis of the MAX Problem in Genetic Programming
TLDR
It is shown that in many cases evolution from the sub-optimal solution to the solution is possible if sucient time is allowed and Price’s Covariance and Selection Theorem is experimentally tested on GP populations.
GOMGE: Gene-Pool Optimal Mixing on Grammatical Evolution
TLDR
GOMGE is presented, the extension of GOMEA to Grammatical Evolution (GE), a popular EA based on an indirect representation which may be applied to any problem whose solutions can be described using a context-free grammar, and two specific improvements aimed at coping with the high degeneracy of those representations.
Solving the exponential growth of symbolic regression trees in geometric semantic genetic programming
TLDR
A new method for individual simplification named GSGP with Reduced trees (GSGP-Red), which is not only possible to create smaller and completely equivalent individuals in competitive computational time, but also to reduce the number of nodes composing them by 58 orders of magnitude, on average.
Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming
TLDR
Results of the experiments suggest that alternating the order of nonlinearity of GP individuals with their structural complexity produces solutions that are both compact and have smoother response surfaces, and, hence, contributes to better interpretability and understanding.
Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors
TLDR
An adaptation of GP-GOMEA to tackle real-world symbolic regression is proposed, in order to find small yet accurate mathematical expressions, and with an application to a problem of clinical interest.
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