- title = "When Short Runs Beat Long Runs",
- author = "Sean Luke",
- pages = "74--80",
- year = "2001",
- publisher = "Morgan Kaufmann",
- booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)",
- editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke",
- address = "San Francisco, California, USA",
- publisher_address = "San Francisco, CA 94104, USA",
- month = "7-11 " # jul,
- keywords = "genetic algorithms, genetic programming, schedules, restarts, run length, critical points",
- ISBN = "1-55860-774-9",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1060.4892",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2001/d01.pdf",
- size = "7 pages",
- abstract = "What will yield the best results: doing one run n generations long or doing m runs n/mgenerations long each? This paper presents a technique-independent analysis which answers this question, and has direct applicability to scheduling and restart theory in evolutionary computation and other stochastic methods. The paper then applies this technique to three problem domains in genetic programming. It discovers that in two of these domains there is a maximal number of generations beyond which it is irrational to plan a run; instead it makes more sense to do multiple shorter runs.",
-
notes = "santafe ant, even-10 parity, symbolic
regression
GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO}",
