- title = "Single- and Multi-Objective Genetic Programming: New Runtime Results for {SORTING}",
- author = "Markus Wagner and Frank Neumann",
- pages = "125--132",
- booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation",
- year = "2014",
- month = "6-11 " # jul,
- editor = "Carlos A. {Coello Coello}",
- address = "Beijing, China",
- ISBN = "0-7803-8515-2",
- keywords = "genetic algorithms, genetic programming, Theoretical Foundations of Bio-inspired Computation",
-
URL = "
http://cs.adelaide.edu.au/~markus/pub/2014cec-sorting.pdf",
-
DOI = "
doi:10.1109/CEC.2014.6900310",
- size = "8 pages",
-
abstract = "In genetic programming, the size of a solution is
typically not specified in advance and solutions of
larger size may have a larger benefit. The flexibility
often comes at the cost of the so-called bloat problem:
individuals grow without providing additional benefit
to the quality of solutions, and the additional
elements can block the optimisation process.
Consequently, problems that are relatively easy to
optimise cannot be handled by variable-length
evolutionary algorithms.
In this article, we present several new bounds for different single and multi-objective algorithms on the sorting problem, a problem that typically lacks independent and additive fitness structures.",
- notes = "WCCI2014",
