- author = "Aniko Ekart",
- title = "Shorter Fitness Preserving Genetic Programs",
- booktitle = "Artificial Evolution. 4th European Conference, AE'99, Selected Papers",
- year = "2000",
- editor = "C. Fonlupt and J.-K. Hao and E. Lutton and E. Ronald and M. Schoenauer",
- volume = "1829",
- series = "LNCS",
- pages = "73--83",
- address = "Dunkerque, France",
- month = "3-5 " # nov,
- keywords = "genetic algorithms, genetic programming",
- ISBN = "3-540-67846-8",
-
URL = "
http://www.sztaki.hu/~ekart/ea.ps",
-
URL = "
http://citeseer.ist.psu.edu/496596.html",
-
DOI = "
doi:10.1007/10721187_5",
- abstract = "In the paper a method that moderates code growth in genetic programming is presented. The addressed problem is symbolic regression. A special mutation operator is used for the simplification of programs. If every individual program in each generation is simplified, then the performance of the genetic programming system is slightly worsened. But if simplification is applied as a mutation operator, more compact solutions of the same or better accuracy can be obtained.",
-
notes = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67846-8
{"}Simplification is implemented in Prolog and consists of approximately 250 clauses.{"} Fig 4. plots of fitness (RMS error) times program size",
