Exploring Hyper-heuristic Methodologies with Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InCollection{Woodward:2009:CI,
-
author = "Edmund K. Burke and Mathew R. Hyde and
Graham Kendall and Gabriela Ochoa and Ender Ozcan and
John R. Woodward",
-
title = "Exploring Hyper-heuristic Methodologies with Genetic
Programming",
-
booktitle = "Computational Intelligence",
-
publisher = "Springer",
-
year = "2009",
-
editor = "Christine L. Mumford and Lakhmi C. Jain",
-
volume = "1",
-
series = "Intelligent Systems Reference Library",
-
chapter = "6",
-
pages = "177--201",
-
keywords = "genetic algorithms, genetic programming",
-
isbn13 = "978-3-642-01798-8",
-
URL = "http://www.cs.nott.ac.uk/~gxo/papers/ChapterGPasHH09.pdf",
-
DOI = "doi:10.1007/978-3-642-01799-5_6",
-
abstract = "Hyper-heuristics represent a novel search methodology
that is motivated by the goal of automating the process
of selecting or combining simpler heuristics in order
to solve hard computational search problems. An
extension of the original hyper-heuristic idea is to
generate new heuristics which are not currently known.
These approaches operate on a search space of
heuristics rather than directly on a search space of
solutions to the underlying problem which is the case
with most meta-heuristics implementations. In the
majority of hyper-heuristic studies so far, a framework
is provided with a set of human designed heuristics,
taken from the literature, and with good measures of
performance in practice. A less well studied approach
aims to generate new heuristics from a set of potential
heuristic components. The purpose of this chapter is to
discuss this class of hyper-heuristics, in which
Genetic Programming is the most widely used
methodology. A detailed discussion is presented
including the steps needed to apply this technique,
some representative case studies, a literature review
of related work, and a discussion of relevant issues.
Our aim is to convey the exciting potential of this
innovative approach for automating the heuristic design
process.",
-
size = "26 pages",
- }
Genetic Programming entries for
Edmund Burke
Matthew R Hyde
Graham Kendall
Gabriela Ochoa
Ender Ozcan
John R Woodward
Citations