- author = "Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang",
- title = "Adaptive complexity knee point selection in multi-objective genetic programming for improving generalization of evolutionary feature construction in regression",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2025",
- volume = "26",
- pages = "Article no 28",
- note = "Online first",
- keywords = "genetic algorithms, genetic programming, evolvable hardware",
- ISSN = "1389-2576",
-
DOI = "
10.1007/s10710-025-09525-6",
- abstract = "we propose a novel Adaptive Complexity Knee Point (ACKP) selection strategy for evolutionary multi-objective feature construction in regression tasks. Our approach adaptively selects between minimal complexity knee points, which favor the simplest knee solution, and traditional knee points, based on estimated dataset difficulty. This adaptive mechanism reduces overfitting in noisy scenarios while mitigating underfitting in low-noise datasets by allowing more complex models when appropriate. Comprehensive experiments on 58 real-world datasets demonstrate that ACKP significantly outperforms nine established model selection strategies, and that genetic programming with ACKP outperforms several mainstream machine learning algorithms, particularly when dealing with sample-limited and noisy datasets, highlighting its practical value in real-world problems.",
-
notes = "
Table S1: Detailed statistical results of test R2 scores for various algorithms across different dataset https://static-content.springer.com/esm/art%3A10.1007%2Fs10710-025-09525-6/MediaObjects/10710_2025_9525_MOESM1_ESM.pdf
School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, 6140, New Zealand",
