Genetic programming for evolving programs with loop structures for classification tasks

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Abdulhamid:2011:ICARA,

• author = "Fahmi Abdulhamid and Kourosh Neshatian and Mengjie Zhang",
• title = "Genetic programming for evolving programs with loop structures for classification tasks",
• booktitle = "5th International Conference on Automation, Robotics and Applications (ICARA 2011)",
• year = "2011",
• month = "6-8 " # dec,
• pages = "202--207",
• address = "Wellington, New Zealand",
• size = "6 pages",
• abstract = "Object recognition and classification are important tasks in robotics. Genetic Programming (GP) is a powerful technique that has been successfully used to automatically generate (evolve) classifiers. The effectiveness of GP is limited by the expressiveness of the functions used to evolve programs. It is believed that loop structures can considerably improve the quality of GP programs in terms of both performance and interpretability. This paper proposes five new loop structures using which GP can evolve compact programs that can perform sophisticated processing. The use of loop structures in GP is evaluated against GP with no loops for both image and non-image classification tasks. Evolved programs using the proposed loop structures are analysed in several problems. The results show that loop structures can increase classification accuracy compared to GP with no loops.",
• keywords = "genetic algorithms, genetic programming, evolving program, image classification task, nonimage classification task, object classification task, object recognition task, program loop structure, robotics, image classification, learning (artificial intelligence), object recognition, robot vision",
• DOI = "doi:10.1109/ICARA.2011.6144882",
• notes = "Also known as \cite{6144882}",

}

Genetic Programming entries for Fahmi Abdulhamid Kourosh Neshatian Mengjie Zhang

Citations