- author = "Marc S. Atkin and Paul R. Cohen",
- title = "Learning monitoring strategies: A difficult genetic programming application",
- booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence",
- year = "1994",
- volume = "1",
- pages = "328--332a",
- address = "Orlando, Florida, USA",
- month = "27-29 " # jun,
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, cupcake problem, agent control language, genetic programming application, monitoring strategy learning, optimal strategies, possible behaviour, learning (artificial intelligence), monitoring; optimisation",
-
URL = "
http://www-eksl.cs.umass.edu/papers/AtkinIEEE.pdf",
-
URL = "
http://citeseer.ist.psu.edu/94049.html",
-
DOI = "
doi:10.1109/ICEC.1994.349931",
- size = "6 pages",
- abstract = "Finding optimal or at least good monitoring strategies is an important consideration when designing an agent. We have applied genetic programming to this task, with mixed results. Since the agent control language was kept purposefully general, the set of monitoring strategies constitutes only a small part of the overall space of possible behaviours. Because of this, it was often difficult for the genetic algorithm to evolve them, even though their performance was superior. These results raise questions as to how easy it will be for genetic programming to scale up as the areas it is applied to become more complex.",
-
notes = "Novel? chrome/program structure linear, close to
assembly language, used GOTOs and interrupt handlers.
Did _not_ get performance improvement on changing to
parse trees. Did evolve progs to control agents which
moved to the goal without colliding with an obstacle.
Finally cautions about problems with GP scaling
up.
{"}Also tried local mating (also known as fine grain parallelism){"}
Also available as Technical Report 94-52, Dept. of Computer Science, University of Massachusetts/Amherst, USA?",
