Name
Abstract | ||
|---|---|---|
Genetic Programming (GP) schemas are structural templates equivalent to hyperplanes in the search space. Schema theories provide information about the properties of subsets of the population and the behavior of genetic operators. In this paper we propose a practical methodology to identify relevant schemas and measure their frequency in the population. We demonstrate our approach on an artificial symbolic regression benchmark where the parts of the formula are already known. Experimental results reveal how solutions are assembled within GP and explain diversity loss in GP populations through the proliferation of repeated patterns. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-74718-7_52 | COMPUTER AIDED SYSTEMS THEORY - EUROCAST 2017, PT I |
Keywords | Field | DocType |
Genetic Programming, Schema analysis, Symbolic regression, Tree pattern matching, Evolutionary dynamics, Loss of diversity | Population,Computer science,Genetic programming,Theoretical computer science,Operator (computer programming),Evolutionary dynamics,Hyperplane,Schema (psychology),Symbolic regression | Conference |
Volume | ISSN | Citations |
10671 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bogdan Burlacu | 1 | 21 | 4.85 |
| Michael Affenzeller | 2 | 339 | 62.47 |
| Michael Kommenda | 3 | 97 | 15.58 |
| Gabriel Kronberger | 4 | 192 | 25.40 |
| Stephan M. Winkler | 5 | 140 | 22.90 |