Gene Expression Programming With Structured Reusability
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| Publicado en: | International Journal of Cognitive Informatics & Natural Intelligence. vol. 19, no. 1 (2025), p. 1-28 |
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| Outros autores: | , , |
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| Acceso en liña: | Citation/Abstract Full Text - PDF |
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| 001 | 3180558195 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1557-3958 | ||
| 022 | |a 1557-3966 | ||
| 024 | 7 | |a 10.4018/IJCINI.371417 |2 doi | |
| 035 | |a 3180558195 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Zheng, YaPing |u Guangzhou University, China | |
| 245 | 1 | |a Gene Expression Programming With Structured Reusability | |
| 260 | |b IGI Global |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In the field of evolutionary computation, gene expression programming (GEP) has been favored by the public because of its straightforward encoding method. Due to the coding rules for the head and tail on gene, the ability of gene to be constructed as an expression is limited, thereby limiting the expressiveness of individuals. In this article, a variant that uses the standard GEP individual structure and is based on code reuse strategy is proposed to maintain the concise individual representation advantage of GEP and improve the expressive ability of individual. This method can improve the accuracy of the individual and reduce the expression tree complexity. In addition, experiments conducted to predict the recurrence of cervical cancer and malignancy of breast cancer have demonstrated that the predicted performance of GEP and gene expression programming with structured reusability (SR-GEP) has performed better than that of C5.0, but SR-GEP performs best. | |
| 653 | |a Stand structure | ||
| 653 | |a Cervical cancer | ||
| 653 | |a Evolutionary computation | ||
| 653 | |a Gene expression | ||
| 653 | |a Performance prediction | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Chromosomes | ||
| 653 | |a Programming | ||
| 653 | |a Standardization | ||
| 653 | |a Informatics | ||
| 653 | |a Code reuse | ||
| 653 | |a Malignancy | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a He, Pei |u Guangzhou University, China | |
| 700 | 1 | |a Chang, Chi-Chang |u School of Medical Informatics, Chung Shan Medical University & IT Office, Chung Shan Medical University Hospital, Taichung, Taiwan & Department of Information Management, Ming Chuan University, Taoyuan City, Taiwan | |
| 700 | 1 | |a Li, Kangshun |u Dongguan City University, China | |
| 773 | 0 | |t International Journal of Cognitive Informatics & Natural Intelligence. |g vol. 19, no. 1 (2025), p. 1-28 | |
| 786 | 0 | |d ProQuest |t Biological Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3180558195/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3180558195/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |