On the Synthesis of Perturbative Heuristics for Multiple Combinatorial Optimisation Domains

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{DBLP:conf/ppsn/StoneHP18,

• author = "Christopher Stone and Emma Hart and Ben Paechter",
• title = "On the Synthesis of Perturbative Heuristics for Multiple Combinatorial Optimisation Domains",
• booktitle = "Parallel Problem Solving from Nature - {PPSN} {XV} - 15th International Conference, Part {I}",
• year = "2018",
• editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and L. Darrell Whitley",
• volume = "11101",
• series = "Lecture Notes in Computer Science",
• pages = "170--182",
• address = "Coimbra, Portugal",
• month = sep # " 8-12",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution, PonyGE2, TSPLIB",
• DOI = "doi:10.1007/978-3-319-99253-2_14",
• timestamp = "Tue, 11 Feb 2020 11:20:56 +0100",
• biburl = "https://dblp.org/rec/conf/ppsn/StoneHP18.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• abstract = "Hyper-heuristic frameworks, although intended to be cross-domain at the highest level, rely on a set of domain-specific low-level heuristics at lower levels. For some domains, there is a lack of available heuristics, while for novel problems, no heuristics might exist. We address this issue by introducing a novel method, applicable in multiple domains, that constructs new low-level heuristics for a domain. The method uses grammatical evolution to construct iterated local search heuristics: it can be considered cross-domain in that the same grammar can evolve heuristics in multiple domains without requiring any modification, assuming that solutions are represented in the same form. We evaluate the method using benchmarks from the traveling-salesman (TSP) and multi-dimensional knapsack (MKP) domain. Comparison to existing methods demonstrates that the approach generates low-level heuristics that outperform heuristic methods for TSP and are competitive for MKP.",

}

Genetic Programming entries for Christopher Stone Emma Hart Ben Paechter

Citations