- author = "Sean Harris and Travis Bueter and Daniel R. Tauritz",
- title = "A Comparison of Genetic Programming Variants for Hyper-Heuristics",
- booktitle = "GECCO 2015 5th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA'15)",
- year = "2015",
- editor = "John Woodward and Daniel Tauritz and Manuel Lopez-Ibanez",
- isbn13 = "978-1-4503-3488-4",
- keywords = "genetic algorithms, genetic programming",
- pages = "1043--1050",
- month = "11-15 " # jul,
- organisation = "SIGEVO",
- address = "Madrid, Spain",
-
URL = "
http://doi.acm.org/10.1145/2739482.2768456",
-
DOI = "
doi:10.1145/2739482.2768456",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "General-purpose optimization algorithms are often not
well suited for real-world scenarios where many
instances of the same problem class need to be
repeatedly and efficiently solved. Hyper-heuristics
automate the design of algorithms for a particular
scenario, making them a good match for real-world
problem solving. For instance, hardware model checking
induced Boolean Satisfiability Problem (SAT) instances
have a very specific distribution which general SAT
solvers are not necessarily well targeted to.
Hyper-heuristics can automate the design of a SAT
solver customized to a specific distribution of SAT
instances.
The first step in employing a hyper-heuristic is creating a set of algorithmic primitives appropriate for tackling a specific problem class. The second step is searching the associated algorithmic primitive space. Hyper-heuristics have typically employed Genetic Programming (GP) to execute the second step, but even in GP there are many alternatives. This paper reports on an investigation of the relationship between the choice of GP type and the performance obtained by a hyper-heuristic employing it. Results are presented on SAT, demonstrating the existence of problems for which there is a statistically significant performance differential between the use of different GP types.",
- notes = "Also known as \cite{2768456} Distributed at GECCO-2015.",
