Abstract
Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random sub-branches of the parents' trees are swapped to create the offspring. Later Miller and Thomson introduced Cartesian Genetic Programming, which uses directed graphs as a representation to replace the tree structures originally introduced by Koza. Cartesian Genetic Programming has been shown to perform better than the traditional Genetic Programming; but it does not; use crossover to create offspring, it is implemented using mutation only. In this paper a new crossover method in Genetic Programming is introduced. The new technique is based on an adaptation of the Cartesian Genetic Programming representation and is tested oil two simple regression problems. It is shown that by implementing the new crossover technique, convergence is faster than that of using mutation only in the Cartesian Genetic Programming method.
| Original language | English |
|---|---|
| Title of host publication | GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2 |
| Place of Publication | NEW YORK |
| Publisher | ACM |
| Pages | 1580-1587 |
| Number of pages | 8 |
| ISBN (Print) | 978-1-59593-697-4 |
| Publication status | Published - 2007 |
| Event | GECCO 2007 - London, England Duration: 7 Jul 2007 → 11 Jul 2007 |
Conference
| Conference | GECCO 2007 |
|---|---|
| City | London, England |
| Period | 7/07/07 → 11/07/07 |
Keywords
- Cartesian Genetic Programming
- optimization
- crossover techniques
- NEUTRALITY
- LANDSCAPE