Name
Abstract | ||
|---|---|---|
Genetic programming (GP) is an evolutionary computation paradigm for automatic program induction. GP has produced impressive results but it still needs to overcome some practical limitations, particularly its high computational cost, overfitting and excessive code growth. Recently, many researchers have proposed fitness-case sampling methods to overcome some of these problems, with mixed results in several limited tests. This paper presents an extensive comparative study of four fitness-case sampling methods, namely: Interleaved Sampling, Random Interleaved Sampling, Lexicase Selection and Keep-Worst Interleaved Sampling. The algorithms are compared on 11 symbolic regression problems and 11 supervised classification problems, using 10 synthetic benchmarks and 12 real-world data-sets. They are evaluated based on test performance, overfitting and average program size, comparing them with a standard GP search. Comparisons are carried out using non-parametric multigroup tests and post hoc pairwise statistical tests. The experimental results suggest that fitness-case sampling methods are particularly useful for difficult real-world symbolic regression problems, improving performance, reducing overfitting and limiting code growth. On the other hand, it seems that fitness-case sampling cannot improve upon GP performance when considering supervised binary classification. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1080/0952813X.2017.1328461 | JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Genetic programming,fitness-case sampling,performance evaluation | Pairwise comparison,Computer science,Evolutionary computation,Genetic programming,Sampling (statistics),Artificial intelligence,Overfitting,Symbolic regression,Machine learning,Code growth | Journal |
Volume | Issue | ISSN |
29.0 | 6 | 0952-813X |
Citations | PageRank | References |
2 | 0.39 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuliana Martínez | 1 | 42 | 5.70 |
| Enrique Naredo | 2 | 49 | 5.55 |
| Leonardo Trujillo | 3 | 444 | 38.12 |
| Pierrick Legrand | 4 | 90 | 16.20 |
| Uriel López | 5 | 3 | 1.46 |