Ensemble Representation Learning: An Analysis of Fitness and Survival for Wrapper-based Genetic Programming Methods

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

@InProceedings{LaCava:2017:GECCO,

• author = "William {La Cava} and Jason H. Moore",
• title = "Ensemble Representation Learning: An Analysis of Fitness and Survival for Wrapper-based Genetic Programming Methods",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4920-8",
• address = "Berlin, Germany",
• pages = "961--968",
• size = "8 pages",
• URL = "http://doi.acm.org/10.1145/3071178.3071215",
• DOI = "doi:10.1145/3071178.3071215",
• acmid = "3071215",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, classification, feature engineering, representation learning",
• month = "15-19 " # jul,
• abstract = "Recently we proposed a general, ensemble-based feature engineering wrapper (FEW) that was paired with a number of machine learning methods to solve regression problems. Here, we adapt FEW for supervised classification and perform a thorough analysis of fitness and survival methods within this framework. Our tests demonstrate that two fitness metrics, one introduced as an adaptation of the silhouette score, outperform the more commonly used Fisher criterion. We analyse survival methods and demonstrate that epsilon-lexicase survival works best across our test problems, followed by random survival which outperforms both tournament and deterministic crowding. We conduct a benchmark comparison to several classification methods using a large set of problems and show that FEW can improve the best classifier performance in several cases. We show that FEW generates consistent, meaningful features for a biomedical problem with different ML pairings.",
• notes = "Also known as \cite{LaCava:2017:ERL:3071178.3071215} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for William La Cava Jason H Moore

Citations