- author = "Bart Wyns and Stefan Sette and Luc Boullart",
- title = "Self-Improvement to Control Code Growth in Genetic Programming",
- booktitle = "Evolution Artificielle, 6th International Conference",
- year = "2003",
- editor = "Pierre Liardet and Pierre Collet and Cyril Fonlupt and Evelyne Lutton and Marc Schoenauer",
- volume = "2936",
- series = "Lecture Notes in Computer Science",
- pages = "256--266",
- address = "Marseilles, France",
- month = "27-30 " # oct,
- publisher = "Springer",
- note = "Revised Selected Papers",
- keywords = "genetic algorithms, genetic programming, Artificial Evolution",
- ISBN = "3-540-21523-9",
-
DOI = "
doi:10.1007/b96080",
- size = "doi:10.1007/978-3-540-24621-3_21",
- abstract = "An important problem with genetic programming systems is that in the course of evolution the size of individuals is continuously growing without a corresponding increase in fitness. This paper reports the application of a self-improvement operator in combination with a characteristic based selection strategy to a classical genetic programming system in order to reduce the effects of code growth. Two examples, a symbolic regression problem and an 11-bit multiplexer problem are used to test and validate the performance of this newly designed operator. Instead of simply editing out non-functional code this method tries to select subtrees with better fitness. Results show that for both test cases code growth is substantially reduced obtaining a reduction factor of 3--10 (depending on the problem) while the same level of fitness is attained.",
- bibsource = "DBLP, http://dblp.uni-trier.de",
-
notes = "EA'03
bloat",
