- author = "John R. Woodward and Jerry Swan",
- title = "The automatic generation of mutation operators for genetic algorithms",
- booktitle = "GECCO 2012 2nd Workshop on Evolutionary Computation for the Automated Design of Algorithms",
- year = "2012",
- editor = "Gisele L. Pappa and John Woodward and Matthew R. Hyde and Jerry Swan",
- isbn13 = "978-1-4503-1178-6",
- keywords = "genetic algorithms, genetic programming, automatic design, hyper-heuristics",
- pages = "67--74",
- month = "7-11 " # jul,
- organisation = "SIGEVO",
- address = "Philadelphia, Pennsylvania, USA",
-
DOI = "
doi:10.1145/2330784.2330796",
- acmid = "2330796",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- abstract = "We automatically generate mutation operators for Genetic Algorithms (GA) and tune them to problem instances drawn from a given problem class. By so doing, we perform metalearning in which the base-level contains GAs (which learn about problem instances), and the meta-level contains GAmutation operators (which learn about problem classes). We use Register Machines to explore a constrained design space for mutation operators. We show how two commonly used mutation operators (viz. one-point and uniform mutation) can be expressed in this framework. Iterated local search is used to search the space of mutation operators, and on a test-bed of 7 problem classes we identify machine-designed mutation operators which outperform their human counterparts.",
-
notes = "Also known as \cite{2330796} and
\cite{Woodward:2012:AGM:2330784.2330796} Distributed at
GECCO-2012.
ACM Order Number 910122.",
