A New Dual-Mode GEP Prediction Algorithm Based on Irregularity and Similar Period. (16th September 2021)
- Record Type:
- Journal Article
- Title:
- A New Dual-Mode GEP Prediction Algorithm Based on Irregularity and Similar Period. (16th September 2021)
- Main Title:
- A New Dual-Mode GEP Prediction Algorithm Based on Irregularity and Similar Period
- Authors:
- Yang, Lei
Xu, Zexin
Xu, Rui
Lu, Jianfan
Xu, Zhenlin
Li, Kangshun - Other Names:
- Cheng Shi Academic Editor.
- Abstract:
- Abstract : Gene expression programming (GEP) uses simple linear coding to solve complex modeling problems. However, the performance is limited by the effectiveness of the selected method of evaluating population individuals, the breadth and depth of the search domain for the solution, and the ability of accuracy of correcting the solution based on historical data. Therefore, a new dual-mode GEP prediction algorithm based on irregularity and similar period is proposed. It takes measures to specialize origin data to reserve the elite individuals, reevaluate the target individuals, and process data and solutions via the similar period mode, which avoids the tendency to get stuck in local optimum and the complexity of the precisions of correcting complex modeling problems due to insufficiency scope of the search domain, and subsequently, better convergence results are obtained. If we take the leek price and the sunspot observation data as the sample to compare the new algorithm with the GEP simulation test, the results indicate that the new algorithm possesses more powerful exploration ability and higher precision. Under the same accuracy requirements, the new algorithm can find the individual faster. Additionally, the conclusion can be drawn that the performance of new algorithm is better on the condition that we take another set of sunspot observations as samples, combining the ARIMA algorithm and BP neural network prediction algorithm for simulation and comparison with theAbstract : Gene expression programming (GEP) uses simple linear coding to solve complex modeling problems. However, the performance is limited by the effectiveness of the selected method of evaluating population individuals, the breadth and depth of the search domain for the solution, and the ability of accuracy of correcting the solution based on historical data. Therefore, a new dual-mode GEP prediction algorithm based on irregularity and similar period is proposed. It takes measures to specialize origin data to reserve the elite individuals, reevaluate the target individuals, and process data and solutions via the similar period mode, which avoids the tendency to get stuck in local optimum and the complexity of the precisions of correcting complex modeling problems due to insufficiency scope of the search domain, and subsequently, better convergence results are obtained. If we take the leek price and the sunspot observation data as the sample to compare the new algorithm with the GEP simulation test, the results indicate that the new algorithm possesses more powerful exploration ability and higher precision. Under the same accuracy requirements, the new algorithm can find the individual faster. Additionally, the conclusion can be drawn that the performance of new algorithm is better on the condition that we take another set of sunspot observations as samples, combining the ARIMA algorithm and BP neural network prediction algorithm for simulation and comparison with the new algorithm. … (more)
- Is Part Of:
- Discrete dynamics in nature and society. Volume 2021(2021)
- Journal:
- Discrete dynamics in nature and society
- Issue:
- Volume 2021(2021)
- Issue Display:
- Volume 2021, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 2021
- Issue:
- 2021
- Issue Sort Value:
- 2021-2021-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-09-16
- Subjects:
- System analysis -- Periodicals
Dynamics -- Periodicals
Chaotic behavior in systems -- Periodicals
Differentiable dynamical systems -- Periodicals
003.05 - Journal URLs:
- https://www.hindawi.com/journals/ddns/ ↗
- DOI:
- 10.1155/2021/3594271 ↗
- Languages:
- English
- ISSNs:
- 1026-0226
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 19437.xml