- author = "Markus Wagner and Frank Neumann and Tommaso Urli",
- title = "On the Performance of Different Genetic Programming Approaches for the SORTING Problem",
- journal = "Evolutionary Computation",
- year = "2015",
- volume = "23",
- number = "4",
- pages = "583--609",
- month = "Winter",
- keywords = "genetic algorithms, genetic programming, Computational complexity, genetic programming, variable-length representation, sortedness, single-objective optimisation, multi-objective optimization",
- ISSN = "1063-6560",
-
DOI = "
doi:10.1162/EVCO_a_00149",
- size = "27 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 analyse different single- and multi-objective algorithms on the sorting problem, a problem that typically lacks independent and additive fitness structures. We complement the theoretical results with comprehensive experiments to indicate the tightness of existing bounds, and to indicate bounds where theoretical results are missing.",
- notes = "The University of Adelaide, Australia. DIEGM, Universita degli Studi di Udine, Udine, Italy",
