A genetic programming hyper-heuristic: Turning features into heuristics for constraint satisfaction

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Ortiz-Bayliss:2013:UKCI,

• author = "Jose Carlos Ortiz-Bayliss and Ender Ozcan and Andrew J. Parkes and Hugo Terashima-Marin",
• booktitle = "13th UK Workshop on Computational Intelligence (UKCI 2013)",
• title = "A genetic programming hyper-heuristic: Turning features into heuristics for constraint satisfaction",
• year = "2013",
• month = "9-11 " # sep,
• pages = "183--190",
• address = "Guildford",
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, constraint satisfaction problems",
• DOI = "doi:10.1109/UKCI.2013.6651304",
• abstract = "A constraint satisfaction problem (CSP) is a combinatorial optimisation problem with many real world applications. One of the key aspects to consider when solving a CSP is the order in which the variables are selected to be instantiated. In this study, we describe a genetic programming hyper-heuristic approach to automatically produce heuristics for CSPs. Human-designed `standard' heuristics are used as components enabling the construction of new variable ordering heuristics which is achieved through the proposed approach. We present empirical evidence that the heuristics produced by our approach are competitive considering the cost of the search when compared to the standard heuristics which are used to obtain the components for the new heuristics. The proposed approach is able to produce specialised heuristics for specific classes of instances that outperform the best standard heuristics for the same instances.",
• notes = "Also known as \cite{6651304}",

}

Genetic Programming entries for Jose Carlos Ortiz-Bayliss Ender Ozcan Andrew J Parkes Hugo Terashima-Marin

Citations