- author = "Michael F. Korns",
- title = "An Evolutionary Algorithm for Big Data Multi-class Classification Problems",
- booktitle = "Genetic Programming Theory and Practice XIV",
- year = "2016",
- editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier",
- pages = "165--178",
- address = "Ann Arbor, USA",
- month = "19-21 " # may,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-3-319-97087-5",
-
URL = "
https://www.springer.com/us/book/9783319970875",
-
DOI = "
doi:10.1007/978-3-319-97088-2_11",
- abstract = "As symbolic regression (SR) has advanced into the early stages of commercial exploitation, the poor accuracy of SR still plagues even advanced commercial packages, and has become an issue for industrial users. Users expect a correct formula to be returned, especially in cases with zero noise and only one basis function with minimal complexity. At a minimum, users expect the response surface of the SR tool to be easily understood, so that the user can know a priori on what classes of problems to expect excellent, average, or poor accuracy. Poor or unknown accuracy is a hindrance to greater academic and industrial acceptance of SR tools. In several previous papers, we presented a complex algorithm for modern SR, which is extremely accurate for a large class of SR problems on noiseless data. Further research has shown that these extremely accurate SR algorithms also improve accuracy in noisy circumstances, albeit not extreme accuracy. Armed with these SR successes, we naively thought that",
-
notes = "
Part of \cite{Tozier:2016:GPTP} published after the workshop",
