- author = "Michael F. Korns",
- title = "Extreme Accuracy in Symbolic Regression",
- booktitle = "Genetic Programming Theory and Practice XI",
- year = "2013",
- series = "Genetic and Evolutionary Computation",
- editor = "Rick Riolo and Jason H. Moore and Mark Kotanchek",
- publisher = "Springer",
- chapter = "1",
- pages = "1--30",
- address = "Ann Arbor, USA",
- month = "9-11 " # may,
- keywords = "genetic algorithms, genetic programming, Abstract expression grammars, Grammar template, Particle swarm, PSO, Symbolic regression",
- isbn13 = "978-1-4939-0374-0",
-
DOI = "
doi:10.1007/978-1-4939-0375-7_1",
-
abstract = "Although recent advances in symbolic regression (SR)
have promoted the field into the early stages of
commercial exploitation, the poor accuracy of SR is
still plaguing even the most advanced commercial
packages today. Users expect to have the correct
formula returned, especially in cases with zero noise
and only one basis function with minimally complex
grammar depth. Poor accuracy is a hindrance to greater
academic and industrial acceptance of SR tools.
In a previous paper, the poor accuracy of Symbolic Regression was explored, and several classes of test formulae, which prove intractable for SR, were examined. An understanding of why these test problems prove intractable was developed. In another paper a baseline Symbolic Regression algorithm was developed with specific techniques for optimising embedded real numbers constants. These previous steps have placed us in a position to make an attempt at vanquishing the SR accuracy problem.
In this chapter we develop a complex algorithm for modern symbolic regression which is extremely accurate for a large class of Symbolic Regression problems. The class of problems, on which SR is extremely accurate, is described in detail. A definition of extreme accuracy is provided, and an informal argument of extreme SR accuracy is outlined in this chapter. Given the critical importance of accuracy in SR, it is our suspicion that in the future all commercial Symbolic Regression packages will use this algorithm or a substitute for this algorithm.",
-
notes = "
Part of \cite{Riolo:2013:GPTP} published after the workshop in 2013",
