Corpus ID: 102352624

A Novel Continuous Representation of Genetic Programmings using Recurrent Neural Networks for Symbolic Regression

  title={A Novel Continuous Representation of Genetic Programmings using Recurrent Neural Networks for Symbolic Regression},
  author={Aftab Anjum and Fengyang Sun and Lin Wang and Jeff Orchard},
Neuro-encoded expression programming that aims to offer a novel continuous representation of combinatorial encoding for genetic programming methods is proposed in this paper. Genetic programming with linear representation uses nature-inspired operators to tune expressions and finally search out the best explicit function to simulate data. The encoding mechanism is essential for genetic programmings to find a desirable solution efficiently. However, the linear representation methods manipulate… Expand
An adaptive GP-based memetic algorithm for symbolic regression
A GP-based memetic algorithm for symbolic regression, termed as aMeGP ( a daptive Me metic GP ), which can balance global exploration and local exploitation adaptively, is proposed and compared with GP and nonGP-based symbolic regression methods. Expand
Bayesian Symbolic Regression
Interpretability is crucial for machine learning in many scenarios such as quantitative finance, banking, healthcare, etc. Symbolic regression (SR) is a classic interpretable machine learning methodExpand
Automated Symbolic Law Discovery: A Computer Vision Approach
Inspired by the incredible success of deep learning in computer vision, this work tackles the problem of automatically discovering scientific laws from experimental data by adapting various successful network architectures into the symbolic law discovery pipeline, and significantly outperforms the current state of the art in terms of data fitting, discovery rate, and succinctness. Expand
Evolutionary optimization of contexts for phonetic correction in speech recognition systems
The results show the viability of a genetic algorithm as a tool for context optimization, which, added to a post-processing correction based on phonetic representations, can reduce the errors on the recognized speech. Expand
Optimización evolutiva de contextos para la corrección fonética en sistemas de reconocimiento del habla
The results show the viability of a genetic algorithm as a tool for context optimization, which added to a post-processing correction based on phonetic representations is able to reduce the errors on the recognized speech. Expand


Neuro-guided genetic programming: prioritizing evolutionary search with neural networks
This work exploits the idea that the likelihoods of instructions' occurrence in a program can be reasonably well estimated from its input-output behavior using a neural network to bias the choice of instructions used by search operators in GP. Expand
Gene Expression Programming: A New Adaptive Algorithm for Solving Problems
Gene expression programming, a genotype/phenotype genetic algorithm (linear and ramified), is presented here for the first time as a new technique for the creation of computer programs with high efficiency that greatly surpasses existing adaptive techniques. Expand
This contribution introduces analytical programming, a novel method that allows solving various problems from the symbolic regression domain. Symbolic regression was first proposed by J. R. Koza inExpand
Prefix Gene Expression Programming
A new representation scheme based on prefix notation that overcomes the original GEP's drawbacks is proposed and the resulted algorithm is called Prefix GEP (P- GEP), which follows a faster fitness convergence curve and the rules generated from P-GEP consistently achieve better average classification accuracy. Expand
Automatically Defined Functions in Gene Expression Programming
In this chapter it is shown how Automatically Defined Functions are encoded in the genotype/phenotype system of Gene Expression programming and how they are implemented in Gene Expression Programming. Expand
Sensitivity-like analysis for feature selection in genetic programming
It is demonstrated that genetic programming introduces many features into evolved models that have little impact on a given model's behaviour, and this can mask the true importance of salient features, so a combination of adaptive terminal selection and bloat control methods may help to improve generalisation performance. Expand
FFX: Fast, Scalable, Deterministic Symbolic Regression Technology
A new non-evolutionary technique for symbolic regression that is orders of magnitude faster than competent GP approaches on real-world problems, returns simpler models, has comparable or better prediction on unseen data, and converges reliably and deterministically. Expand
Genetic Programming: An Introduction and Tutorial, with a Survey of Techniques and Applications
This chapter introduces genetic programming (GP) a set of evolutionary computation techniques for getting computers to automatically solve problems without having to tell them explicitly how to doExpand
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Expand
Evolving Evolutionary Algorithms Using Multi Expression Programming
An nongenerational EA for function optimization is evolved in this paper and numerical experiments show the effectiveness of this approach. Expand