- author = "John H. Drake and Matthew Hyde and Khaled Ibrahim and Ender Ozcan",
- title = "A Genetic Programming Hyper-Heuristic for the Multidimensional Knapsack Problem",
- year = "2012",
- booktitle = "11th IEEE International Conference on Cybernetic Intelligent Systems",
- editor = "N. H. Siddique and Michael O'Grady",
- address = "Limerick, Ireland",
- month = "23-24 " # aug,
- organisation = "IEEE Systems, Man and Cybernetics Society with the theme of Cybernetic Intelligent Systems",
- keywords = "genetic algorithms, genetic programming, hyper-heuristics, heuristic generation, multidimensional knapsack problem",
- annote = "The Pennsylvania State University CiteSeerX Archives",
- bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
- language = "en",
- oai = "oai:CiteSeerX.psu:10.1.1.368.5880",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.368.5880",
-
URL = "
http://www.cs.nott.ac.uk/~exo/docs/publications/cis2012_GP_MKP.pdf",
- size = "5 pages",
- abstract = "Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. Early hyperheuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. This work investigates the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results.",
-
notes = "May 2014 not in IEEE
xplor.
http://cis2012.wordpress.com/conference-programme/",
