A hybrid gene expression programming algorithm based on orthogonal design

Yang, Jie; Ma, Jun

doi:10.1080/18756891.2016.1204124

Cited by 10 publications

(2 citation statements)

References 31 publications

(42 reference statements)

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“…The experimental results showed that the proposed algorithm improved performances over several state-of-the-art algorithms in terms of accuracy and search efficiency. Yang and Ma [21] introduced an orthogonal design method into gene expression programming and proposed a new hybrid GEP algorithm. The comparative results demonstrated that the proposed algorithm had a better generalization ability than other traditional algorithms.…”

Section: B Gene Expression Programmingmentioning

confidence: 99%

A Novel Function Mining Algorithm Based on Attribute Reduction and Improved Gene Expression Programming

et al. 2019

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It is very interesting and important to determine the function model for remote-sensing data. The existing statistical and artificial intelligence models still have some defects. The statistical models rely heavily on prior knowledge and cannot objectively reflect the real function model contained in the remote-sensing data. In addition, the existing artificial intelligence models can very easily fall into the local optimum and have a low efficiency for high-dimensional remote-sensing data. In this paper, we first decrease the complexity of remote-sensing data by using rough sets and propose an attribute reduction algorithm based on rough sets for remote-sensing data (ARRS-RSD). On the basis of the algorithm, this paper presents a function mining algorithm for remote-sensing data by using gene expression programming and rough sets (FMRS-ARGEP). In FMRS-ARGEP, a dynamic population generation policy and a new mutation operation based on self-adaptive rate adjustment are introduced to improve the convergence of the algorithm. The experimental results show that the proposed algorithm outperforms traditional algorithms in terms of the average running time, the number of condition attributes after reduction, the attribute reduction ratio, the average convergence speed, the number of convergences, and the R 2 value of the model.

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Section: B Gene Expression Programmingmentioning

confidence: 99%

A Novel Function Mining Algorithm Based on Attribute Reduction and Improved Gene Expression Programming

et al. 2019

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“…Zhang et al [6] and Zuo et al [7] combined GEP with differential evolution and found that their proposed algorithm performed better than the original GEP. Yang and Ma [8] used an orthogonal design for a multiple-parent crossover operator in chromosome reproduction. The study also introduced an evolutionary stable strategy to maintain population diver-sity during evolution.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Length Gene Expression Programming

Amar,

Sudiarso,

Kusumawan

2023

IJCDS

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Gene expression programming (GEP) is capable of solving many prediction, classification, and optimization problem effectively. It uses a fixed-length chromosome representing a set of equations. However, the chromosome length significantly affects the algorithm's performance. Different problems may require varied chromosome lengths to achieve good results. Only a few studies have been conducted to deal with chromosome length in GEP. Therefore, this study aimed to develop an adaptive GEP to find proper chromosome length during the evolutionary process. The study proposed that the chromosome length may be varied for each individual instead of using the same length in the population. The evolutionary process would adjust the chromosome length and the chromosome with proper length will tend to survive. Furthermore, the study proposed a contraction operator that could delete or insert an allele in the chromosome to make it short or extended. This operator is expected to adjust the chromosome length to its optimal. A special slice crossover was also proposed to accommodate the crossover between parents with different chromosome lengths. The proposed algorithms' performance was investigated by solving three symbolic regression problems. Additionally, the performance was compared to related previous gene expression programming algorithms.

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