- author = "Eric Medvet and Alberto Bartoli and Andrea {De Lorenzo} and Fabiano Tarlao",
- title = "{GOMGE}: Gene-pool Optimal Mixing on Grammatical Evolution",
- booktitle = "15th International Conference on Parallel Problem Solving from Nature",
- year = "2018",
- editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley",
- volume = "11101",
- series = "LNCS",
- pages = "223--235",
- address = "Coimbra, Portugal",
- month = "8-12 " # sep,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Linkage, Family of Subsets, Representation",
- isbn13 = "978-3-319-99252-5",
-
URL = "
https://www.springer.com/gp/book/9783319992587",
-
DOI = "
doi:10.1007/978-3-319-99253-2_18",
- abstract = "Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a recent Evolutionary Algorithm (EA) in which the interactions among parts of the solution (i.e., the linkage) are learned and exploited in a novel variation operator. We present GOMGE, the extension of GOMEA to Grammatical Evolution (GE), a popular EA based on an indirect representation which may be applied to any problem whose solutions can be described using a context-free grammar (CFG). GE is a general approach that does not require the user to tune the internals of the EA to fit the problem at hand: there is hence the opportunity for benefiting from the potential of GOMEA to automatically learn and exploit the linkage. We apply the proposed approach to three variants of GE differing in the representation (original GE, SGE, and WHGE) and incorporate in GOMGE two specific improvements aimed at coping with the high degeneracy of those representations. We experimentally assess GOMGE and show that, when coupled with WHGE and SGE, it is clearly beneficial to both effectiveness and efficiency, whereas it delivers mixed results with the original GE.",
-
notes = "PPSN2018 http://ppsn2018.dei.uc.pt
This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018",
