Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Feature-based classification of time-series data
Information processing and technology
Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming
SYNASC '05 Proceedings of the Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Characteristic-Based Clustering for Time Series Data
Data Mining and Knowledge Discovery
Genetic programming for epileptic pattern recognition in electroencephalographic signals
Applied Soft Computing
Parsimony doesn't mean simplicity: genetic programming for inductive inference on noisy data
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Evolving stochastic processes using feature tests and genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Inference of gene expression networks using memetic gene expression programming
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
| Hi-index | 0.00 |
Symbolic regression is a popular genetic programming (GP) application. Typically, the fitness function for this task is based on a sum-of-errors, involving the values of the dependent variable directly calculated from the candidate expression. While this approach is extremely successful in many instances, its performance can deteriorate in the presence of noise. In this paper, a feature-based fitness function is considered, in which the fitness scores are determined by comparing the statistical features of the sequence of values, rather than the actual values themselves. The set of features used in the fitness evaluation are customized according to the target, and are drawn from a wide set of features capable of characterizing a variety of behaviours. Experiments examining the performance of the feature-based and standard fitness functions are carried out for non-oscillating and oscillating targets in a GP system which introduces noise during the evaluation of candidate expressions. Results show strength in the feature-based fitness function, especially for the oscillating target.