Robust Gene Expression Programming

TL;DR: The comparative results show that the proposed S_GEP method can significantly improve the GEP performance, and a thorough comparative study between this method and the primitive GEP, as well as other methods are included.

TL;DR: The results reveal that the GEP technique outperforms traditional statistical techniques in predicting oil prices and has the highest explanatory power as measured by the R-squared statistic.

TL;DR: In this article, a Gene Expression Programming (GEP) based approach was used for the prediction of viscosities of pure hydrocarbons as well as gas mixtures containing heavy hydrocarbon components and impurities such as carbon dioxide, nitrogen, helium and hydrocarbon sulfide using over 3800 data sets.

TL;DR: The results show that the proposed formulation has good agreement with actual values of climbing rate of slip forming systems with low error and complexity and find it to be quite confident.

TL;DR: This paper presents a meta-anatomy of Gene Expression Programming using the GEP-RNC Algorithm as a guide for problem-solving in Numerical Constants and Neural Networks.

TL;DR: 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.

TL;DR: This technique is a simplification of Gene Expression Programming that is equally efficient and powerful and the underlying representation of a solution to a problem in RGEP is a bit vector as in Genetic Algorithm.

TL;DR: This paper proposes a binary representation of GEP chromosomes to palliate the computation requirements needed, and a theoretical reasoning behind the proposed representation is provided, along with empirical validation.