- author = "David Fagan and Erik Hemberg and Miguel Nicolau and Michael O'Neill and Sean McGarraghy",
- title = "Towards adaptive mutation in grammatical evolution",
- booktitle = "GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion",
- year = "2012",
- editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb",
- isbn13 = "978-1-4503-1178-6",
- keywords = "genetic algorithms, Genetic programming, grammatical evolution: Poster",
- pages = "1481--1482",
- month = "7-11 " # jul,
- organisation = "SIGEVO",
- address = "Philadelphia, Pennsylvania, USA",
-
DOI = "
doi:10.1145/2330784.2331002",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- abstract = "Adaptive mutation operations have been proposed in Evolutionary Computation (EC) many times and in different varieties, but few have gained widespread use. In nature, mutation rates vary over time, however it has become common practice to use static, widely accepted, values for mutation, particularly in GP-like systems. In this study, an adaptive mutation operation is presented and applied to Grammatical Evolution (GE) over a variety of benchmark problems. The results are examined and it is determined that the new operators could replace the need to specify mutation rates in GE on the problem domains examined.",
-
notes = "Also known as \cite{2331002} Distributed at
GECCO-2012.
ACM Order Number 910122.",
