Extremely Accurate Symbolic Regression for Large Feature Problems

Korns, Michael F.

doi:10.1007/978-3-319-16030-6_7

Cited by 8 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In many cases of regression problems, the required model is aimed to be created from a data set that has either an enormous data size or complex relations within the variables and the targeted output, which traditionally, prediction algorithms commonly become impracticable or computationally inefficient solutions . Recently, a novel AI regression technique based on evolutionary algorithms for problems with big data size and complex relations has been proposed (SR) …”

Section: Ai‐based Regression Algorithmsmentioning

confidence: 99%

“…In many cases of regression problems, the required model is aimed to be created from a data set that has either an enormous data size or complex relations within the variables and the targeted output, which traditionally, prediction algorithms commonly become 62,63 Recently, a novel AI regression technique based on evolutionary algorithms for problems with big data size and complex relations has been proposed (SR). [64][65][66][67][68][69][70] Symbolic regression has been inspired by "Darwinian theory of evolution," where there is no any calculated solution. Instead, there are a series of potential solutions that simply can be named as population.…”

Section: Symbolic Regressionmentioning

confidence: 99%

See 1 more Smart Citation

Application of artificial intelligence algorithms on modeling of reflection phase characteristics of a nonuniform reflectarray element

Mahoutı

2019

Int J Numerical Modelling

View full text Add to dashboard Cite

Reflectarray antennas (RAs) have the ability to combine the advantages of both traditional parabolic reflector and phased array antennas without the need for feed network designs. Microstrip reflectarrays (MRAs) have the advantages of being small size, light weighted, easy to prototyped, high gain, low side-lobe level, and a predetermined radiation pattern. These can be achieved by precise calculation of reflection phase at each RA unit independently with a phase compensation proportional to the distance from the feed. The challenging problem is to have a fast and high accurate unit element to be used in multidimension, multiobjective design optimization. Herein, artificial intelligence algorithms (AIAs) have been used for prediction of reflection phase characterization of an X band MRA unit element with respect to the geometrical design parameters. Firstly, a nonuniform unit RA has been designed in 3D electromagnetic (EM) simulation tool for creating the training validation data sets. Then, the data sets are given to the different types of AIA regression models such as multilayer perceptron, symbolic regression, and convolutional neural network. From the results of the validation data set, it can be concluded that the proposed models have sufficient accuracy that can be used in a computationally efficient design optimization process of a large-scale RA design.

show abstract

Section: Ai‐based Regression Algorithmsmentioning

confidence: 99%

“…In many cases of regression problems, the required model is aimed to be created from a data set that has either an enormous data size or complex relations within the variables and the targeted output, which traditionally, prediction algorithms commonly become 62,63 Recently, a novel AI regression technique based on evolutionary algorithms for problems with big data size and complex relations has been proposed (SR). [64][65][66][67][68][69][70] Symbolic regression has been inspired by "Darwinian theory of evolution," where there is no any calculated solution. Instead, there are a series of potential solutions that simply can be named as population.…”

Section: Symbolic Regressionmentioning

confidence: 99%

Application of artificial intelligence algorithms on modeling of reflection phase characteristics of a nonuniform reflectarray element

Mahoutı

2019

Int J Numerical Modelling

View full text Add to dashboard Cite

show abstract

Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication

Kammerer

Kronberger

Burlacu

et al. 2020

Genetic and Evolutionary Computation

View full text Add to dashboard Cite

Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available. Such scenarios often require specific model properties such as interpretability, robustness, trustworthiness and plausibility, that are not easily achievable using standard approaches like genetic programming for symbolic regression. In this chapter we introduce a deterministic symbolic regression algorithm specifically designed to address these issues. The algorithm uses a context-free grammar to produce models that are parameterized by a non-linear least squares local optimization procedure. A finite enumeration of all possible models is guaranteed by structural restrictions as well as a caching mechanism for detecting semantically equivalent solutions. Enumeration order is established via heuristics designed to improve search efficiency. Empirical tests on a comprehensive benchmark suite show that our approach is competitive with genetic programming in many noiseless problems while maintaining desirable properties such as simple, reliable models and reproducibility.

show abstract

Trading Volatility Using Highly Accurate Symbolic Regression

Korns¹

2015

Handbook of Genetic Programming Applications

View full text Add to dashboard Cite

Extremely Accurate Symbolic Regression for Large Feature Problems

Cited by 8 publications

References 11 publications

Application of artificial intelligence algorithms on modeling of reflection phase characteristics of a nonuniform reflectarray element

Application of artificial intelligence algorithms on modeling of reflection phase characteristics of a nonuniform reflectarray element

Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication

Trading Volatility Using Highly Accurate Symbolic Regression

Contact Info

Product

Resources

About