dc.contributor.author | Moraglio, A | |
dc.contributor.author | McDermott, J | |
dc.contributor.author | O'Neill, M | |
dc.date.accessioned | 2017-12-12T10:34:04Z | |
dc.date.issued | 2018-09-12 | |
dc.description.abstract | Geometric Semantic Genetic Programming (GSGP) is a novel form of
Genetic Programming (GP), based on a geometric theory of evolutionary algorithms,
which directly searches the semantic space of programs. In this chapter,
we extend this framework to Grammatical Evolution (GE) and refer to the new
method as Geometric Semantic Grammatical Evolution (GSGE). We formally derive
new mutation and crossover operators for GE which are guaranteed to see a simple
unimodal fitness landscape. This surprising result shows that the GE genotypephenotype
mapping does not necessarily imply low genotype-fitness locality. To
complement the theory, we present extensive experimental results on three standard
domains (Boolean, Arithmetic and Classifier). | en_GB |
dc.identifier.citation | In: Handbook of Grammatical Evolution, edited by Conor Ryan, Michael O'Neill, and J.J. Collins, pp. 163-188. | en_GB |
dc.identifier.doi | 10.1007/978-3-319-78717-6_7 | |
dc.identifier.uri | http://hdl.handle.net/10871/30645 | |
dc.language.iso | en | en_GB |
dc.publisher | Springer | en_GB |
dc.rights.embargoreason | Under embargo until 12 September 2020 in compliance with publisher policy | en_GB |
dc.rights | © Springer International Publishing AG, part of Springer Nature 2018. | |
dc.title | Geometric Semantic Grammatical Evolution | en_GB |
dc.type | Book chapter | en_GB |
dc.description | This is the author accepted manuscript. The final version is available from Springer via the DOI in this record. | en_GB |
dc.identifier.journal | Handbook of Grammatical Evolution | en_GB |