- author = "Riccardo Poli and William B. Langdon",
- title = "Running Genetic Programming Backward",
- booktitle = "Genetic Programming Theory and Practice {III}",
- year = "2005",
- editor = "Tina Yu and Rick L. Riolo and Bill Worzel",
- volume = "9",
- series = "Genetic Programming",
- chapter = "9",
- pages = "125--140",
- address = "Ann Arbor",
- month = "12-14 " # may,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "0-387-28110-X",
-
URL = "
http://www.cs.essex.ac.uk/staff/poli/papers/GPTP2005.pdf",
-
DOI = "
doi:10.1007/0-387-28111-8_9",
- size = "16 pages",
- abstract = "Backward chaining evolutionary algorithms (BC-EA) offer the prospect of runtime efficiency savings by reducing the number of fitness evaluations without significantly changing the course of genetic algorithm (GA) or genetic programming (GP) runs. poli05:_tourn_selec_iterat_coupon_probl described how BC-EA does this by avoiding the generation and evaluation of individuals which never appear in selection tournaments. (Poli,2005) suggested the largest savings occur in very large populations, short runs and small tournament sizes. It gave some evidence of the actual savings in fixed-length binary GAs. Here we provide a generational GP implementation of BC-EA and empirically investigate its efficiency, in terms of both fitness evaluations and effectiveness, with mutation and two offspring crossover.",
-
notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after
the workshop
See also \cite{CSM-425}",
