- author = "Riccardo Poli",
- title = "Evolution of Graph-like Programs with Parallel Distributed Genetic Programming",
- booktitle = "Genetic Algorithms: Proceedings of the Seventh International Conference",
- year = "1997",
- editor = "Thomas Back",
- pages = "346--353",
- address = "Michigan State University, East Lansing, MI, USA",
- publisher_address = "San Francisco, CA, USA",
- month = "19-23 " # jul,
- publisher = "Morgan Kaufmann",
- keywords = "genetic algorithms, genetic programming, PDGP",
- ISBN = "1-55860-487-1",
-
URL = "
http://cswww.essex.ac.uk/staff/rpoli/papers/Poli-ICGA1997-PDGP.pdf",
-
URL = "
http://citeseer.ist.psu.edu/372035.html",
- size = "8 pages",
- abstract = "Parallel Distributed Genetic Programming (PDGP) is a new form of Genetic Programming (GP) suitable for the development of programs with a high degree of parallelism. Programs are represented in PDGP as graphs with nodes representing functions and terminals, and links representing the flow of control and results. The paper presents the representations, the operators and the interpreters used in PDGP, and describes experiments in which PDGP has been compared to standard GP.",
-
notes = "ICGA-97
Here PDGP was firstly applied to the lawnmower problem. On this problem the effort scaled up (as the size of the lawn was increased) 2300 times better than Std GP and it scaled up linearly rather than exponentially. Also the solutions found were between 10 and 30 times smaller. Then PDGP was applied to the MAX problem. Again the effort scaled up linearly (and 170 times better than GP) rather than exponentially.",
