A Comparison of Fitness-Case Sampling Methods for Symbolic Regression with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Martinez:2014:EVOLVE,

• author = "Yuliana Martinez and Leonardo Trujillo and Enrique Naredo and Pierrick Legrand",
• title = "A Comparison of Fitness-Case Sampling Methods for Symbolic Regression with Genetic Programming",
• booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V",
• year = "2014",
• editor = "Alexandru-Adrian Tantar and Emilia Tantar and Jian-Qiao Sun and Wei Zhang and Qian Ding and Oliver Schuetze and Michael Emmerich and Pierrick Legrand and Pierre {Del Moral} and Carlos A. {Coello Coello}",
• volume = "288",
• series = "Advances in Intelligent Systems and Computing",
• pages = "201--212",
• address = "Peking",
• month = "1-4 " # jul,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Fitness-Case Sampling, Symbolic Regression, Performance Evaluation",
• isbn13 = "978-3-319-07493-1",
• DOI = "doi:10.1007/978-3-319-07494-8_14",
• abstract = "The canonical approach towards fitness evaluation in Genetic Programming (GP) is to use a static training set to determine fitness, based on a cost function averaged over all fitness-cases. However, motivated by different goals, researchers have recently proposed several techniques that focus selective pressure on a subset of fitness-cases at each generation. These approaches can be described as fitness-case sampling techniques, where the training set is sampled, in some way, to determine fitness. This paper shows a comprehensive evaluation of some of the most recent sampling methods, using benchmark and real-world problems for symbolic regression. The algorithms considered here are Interleaved Sampling, Random Interleaved Sampling, Lexicase Selection and a new sampling technique is proposed called Keep-Worst Interleaved Sampling (KW-IS). The algorithms are extensively evaluated based on test performance, over fitting and bloat. Results suggest that sampling techniques can improve performance compared with standard GP. While on synthetic benchmarks the difference is slight or none at all, on real-world problems the differences are substantial. Some of the best results were achieved by Lexicase Selection and Keep Worse-Interleaved Sampling. Results also show that on real-world problems overfitting correlates strongly with bloating. Furthermore, the sampling techniques provide efficiency, since they reduce the number of fitness-case evaluations required over an entire run.",

}

Genetic Programming entries for Yuliana Martinez Leonardo Trujillo Enrique Naredo Pierrick Legrand

Citations