- author = "Brian J. Ross",
- title = "The Effects of Randomly Sampled Training Data on Program Evolution",
- pages = "443--450",
- year = "2000",
- publisher = "Morgan Kaufmann",
- booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)",
- editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer",
- address = "Las Vegas, Nevada, USA",
- publisher_address = "San Francisco, CA 94104, USA",
- month = "10-12 " # jul,
- keywords = "genetic algorithms, genetic programming, grammar, stochastic regular expressions",
- ISBN = "1-55860-708-0",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2000/GP003.pdf",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2000/GP003.ps",
-
URL = "http://www.cosc.brocku.ca/~bross/research/gp003.ps",
-
URL = "
http://citeseer.ist.psu.edu/303412.html",
- size = "8 pages",
- abstract = "The effects of randomly sampled training data on genetic programming performance is empirically investigated. Often the most natural, if not only, means of characterising the target behaviour for a problem is to randomly sample training cases inherent to that problem. A natural question to raise about this strategy is, how deleterious is the randomly sampling of training data to evolution performance? Will sampling reduce the evolutionary search to hill climbing? Can re-sampling during the run be advantageous? We address these questions by undertaking a suite of different GP experiments. Parameters include various sampling strategies (single, re-sampling, ideal samples), generational and steady-state evolution, and non-evolutionary strategies such as hill climbing and random search. The experiments confirm that random sampling effectively characterizes stochastic domains during genetic programming, provided that a sufficiently representative sample is used. An unexpected result is that genetic programming may perform worse than random search when the sampled training sets are exceptionally poor. We conjecture that poor training sets cause evolution to prematurely converge to undesirable optima, which irrevocably handicaps the population's diversity and viability.",
-
notes = "pop=750/500 (culled), gens=50. p448 'higher quality
training evolved better quality solutions'. p448 L1 GP
better than hill climbing and random search (not true
on L2).
A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}
See also \cite{oai:CiteSeerPSU:250158}",
