- author = "Nathaniel R. Kamrath and Brian W. Goldman and Daniel R. Tauritz",
- title = "Using Supportive Coevolution to Evolve Self-Configuring Crossover",
- booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion",
- year = "2013",
- editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks",
- isbn13 = "978-1-4503-1964-5",
- keywords = "genetic algorithms, genetic programming",
- pages = "1489--1496",
- month = "6-10 " # jul,
- organisation = "SIGEVO",
- address = "Amsterdam, The Netherlands",
-
URL = "
http://doi.acm.org/10.1145/2464576.2482727",
-
DOI = "
doi:10.1145/2464576.2482727",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "Creating an Evolutionary Algorithm (EA) which is
capable of automatically configuring itself and
dynamically controlling its parameters is a challenging
problem. However, solving this problem can reduce the
amount of manual configuration required to implement an
EA, allow the EA to be more adaptable, and produce
better results on a range of problems without requiring
problem specific tuning. Using Supportive Coevolution
(SuCo) to evolve Self-Configuring Crossover (SCX)
combines the automatic configuration technique of
multiple populations from SuCo with the dynamic
crossover operator creation and evolution of SCX.
This paper reports an empirical comparison and analysis of several different combinations of mutation and crossover techniques including SuCo and SCX. The Rosenbrock, Rastrigin, and Offset Rastrigin benchmark problems were selected for testing purposes. The benefits and drawbacks of self-adaptation and evolution of SCX are also discussed. SuCo of mutation step sizes and SCX operators produced results that were at least as good as previous work, and some experiments produced results that were significantly better.",
- notes = "Also known as \cite{2482727} Distributed at GECCO-2013.",
