Zoetrope Genetic Programming for Regression
- URL: http://arxiv.org/abs/2102.13388v1
- Date: Fri, 26 Feb 2021 10:47:10 GMT
- Title: Zoetrope Genetic Programming for Regression
- Authors: Aur\'elie Boisbunon, Carlo Fanara, Ingrid Grenet, Jonathan Daeden,
Alexis Vighi, Marc Schoenauer
- Abstract summary: The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions.
ZGP is validated using a large number of public domain regression datasets.
- Score: 2.642406403099596
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The Zoetrope Genetic Programming (ZGP) algorithm is based on an original
representation for mathematical expressions, targeting evolutionary symbolic
regression.The zoetropic representation uses repeated fusion operations between
partial expressions, starting from the terminal set. Repeated fusions within an
individual gradually generate more complex expressions, ending up in what can
be viewed as new features. These features are then linearly combined to best
fit the training data. ZGP individuals then undergo specific crossover and
mutation operators, and selection takes place between parents and offspring.
ZGP is validated using a large number of public domain regression datasets, and
compared to other symbolic regression algorithms, as well as to traditional
machine learning algorithms. ZGP reaches state-of-the-art performance with
respect to both types of algorithms, and demonstrates a low computational time
compared to other symbolic regression approaches.
Related papers
- Discovering symbolic expressions with parallelized tree search [59.92040079807524]
Symbolic regression plays a crucial role in scientific research thanks to its capability of discovering concise and interpretable mathematical expressions from data.
Existing algorithms have faced a critical bottleneck of accuracy and efficiency over a decade when handling problems of complexity.
We introduce a parallelized tree search (PTS) model to efficiently distill generic mathematical expressions from limited data.
arXiv Detail & Related papers (2024-07-05T10:41:15Z) - A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming [0.0]
Symbolic regression aims to model and map data in a way that can be understood by scientists.
Recent advancements, have attempted to bridge the gap between these two fields.
arXiv Detail & Related papers (2024-06-05T19:01:43Z) - The Inefficiency of Genetic Programming for Symbolic Regression -- Extended Version [0.0]
We analyse the search behaviour of genetic programming for symbolic regression in practically relevant but limited settings.
This enables us to quantify the success probability of finding the best possible expressions.
We compare the search efficiency of genetic programming to random search in the space of semantically unique expressions.
arXiv Detail & Related papers (2024-04-26T09:49:32Z) - Deep Generative Symbolic Regression [83.04219479605801]
Symbolic regression aims to discover concise closed-form mathematical equations from data.
Existing methods, ranging from search to reinforcement learning, fail to scale with the number of input variables.
We propose an instantiation of our framework, Deep Generative Symbolic Regression.
arXiv Detail & Related papers (2023-12-30T17:05:31Z) - Accelerated Discovery of Machine-Learned Symmetries: Deriving the
Exceptional Lie Groups G2, F4 and E6 [55.41644538483948]
This letter introduces two improved algorithms that significantly speed up the discovery of symmetry transformations.
Given the significant complexity of the exceptional Lie groups, our results demonstrate that this machine-learning method for discovering symmetries is completely general and can be applied to a wide variety of labeled datasets.
arXiv Detail & Related papers (2023-07-10T20:25:44Z) - Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search [29.392036559507755]
Symbolic regression is a problem of learning a symbolic expression from numerical data.
Deep neural models trained on procedurally-generated synthetic datasets showed competitive performance.
We propose a novel method which provides the best of both worlds, based on a Monte-Carlo Tree Search procedure.
arXiv Detail & Related papers (2023-02-22T09:10:20Z) - Symbolic Regression via Neural-Guided Genetic Programming Population
Seeding [6.9501458586819505]
Symbolic regression is a discrete optimization problem generally believed to be NP-hard.
Prior approaches to solving the problem include neural-guided search and genetic programming.
We propose a neural-guided component used to seed the starting population of a random restart genetic programming component.
arXiv Detail & Related papers (2021-10-29T19:26:41Z) - Neural Symbolic Regression that Scales [58.45115548924735]
We introduce the first symbolic regression method that leverages large scale pre-training.
We procedurally generate an unbounded set of equations, and simultaneously pre-train a Transformer to predict the symbolic equation from a corresponding set of input-output-pairs.
arXiv Detail & Related papers (2021-06-11T14:35:22Z) - On Function Approximation in Reinforcement Learning: Optimism in the
Face of Large State Spaces [208.67848059021915]
We study the exploration-exploitation tradeoff at the core of reinforcement learning.
In particular, we prove that the complexity of the function class $mathcalF$ characterizes the complexity of the function.
Our regret bounds are independent of the number of episodes.
arXiv Detail & Related papers (2020-11-09T18:32:22Z) - The data-driven physical-based equations discovery using evolutionary
approach [77.34726150561087]
We describe the algorithm for the mathematical equations discovery from the given observations data.
The algorithm combines genetic programming with the sparse regression.
It could be used for governing analytical equation discovery as well as for partial differential equations (PDE) discovery.
arXiv Detail & Related papers (2020-04-03T17:21:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.