Sign Change Detection based Fitness Evaluation for Automatic Implicit Equation Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{wen:2024:GECCO,

• author = "Jiahao Wen and Junlan Dong and Jinghui Zhong",
• title = "Sign Change Detection based Fitness Evaluation for Automatic Implicit Equation Discovery",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference",
• year = "2024",
• editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva",
• pages = "980--987",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, fitness evaluation, implicit symbolic regression, sign change detection",
• isbn13 = "979-8-4007-0494-9",
• DOI = "doi:10.1145/3638529.3654015",
• size = "8 pages",
• abstract = "Automatic implicit equation discovery is a meaningful and challenging problem in symbolic regression. The current common methods for the automatic discovery of implicit equations include derivative calculations and comprehensive learning. However, both methods come with their own set of challenges. Derivative calculations pose difficulties in handling sparse data. The comprehensive learning method may encounter problems associated with multiple multiplications, making it difficult to find the optimal equation. Inspired by Bolzano's theorem,we propose a new evaluation mechanism known as the {"}Sign Change Detection (SCD) based Fitness Evaluation{"}. The main idea of our proposed mechanism is to approximate the solution of an equation using Bolzano's theorem. This mechanism can overcome the limitations associated with derivative calculations and comprehensive learning methods. Furthermore, we integrate this mechanism with self-learning gene expression programming (SL-GEP) to develop a new SCD-GEP method. Experimental results have shown that the proposed method surpasses the compared approaches in discovering implicit equations, achieving a higher success rate in finding optimal solutions.",
• notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Jiahao Wen Junlan Dong Jinghui Zhong

Citations