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Venues (Conferences, Journals, ...)
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GrowBag graphs for keyword ? (Num. hits/coverage)
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The graphs summarize 9 occurrences of 8 keywords
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Results
Found 7 publication records. Showing 7 according to the selection in the facets
| Hits ?▲ |
Authors |
Title |
Venue |
Year |
Link |
Author keywords |
| 1 | Atanas Kamburov, Rachel Cavill, Timothy M. D. Ebbels, Ralf Herwig, Hector C. Keun |
Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA.  |
Bioinformatics  |
2011 |
DBLP DOI BibTeX RDF |
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| 1 | Rachel Cavill, Atanas Kamburov, James K. Ellis, Toby J. Athersuch, Marcus S. C. Blagrove, Ralf Herwig, Timothy M. D. Ebbels, Hector C. Keun |
Consensus-Phenotype Integration of Transcriptomic and Metabolomic Data Implies a Role for Metabolism in the Chemosensitivity of Tumour Cells. |
PLoS Computational Biology |
2011 |
DBLP DOI BibTeX RDF |
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| 1 | Rachel Cavill, Hector C. Keun, Elaine Holmes, John C. Lindon, Jeremy K. Nicholson, Timothy M. D. Ebbels |
Genetic algorithms for simultaneous variable and sample selection in metabonomics. |
Bioinformatics |
2009 |
DBLP DOI BibTeX RDF |
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| 1 | James Alfred Walker, Julian Francis Miller, Rachel Cavill |
A multi-chromosome approach to standard and embedded cartesian genetic programming. |
GECCO |
2006 |
DBLP DOI BibTeX RDF |
automatically defined functions, embedded cartesian genetic programming, multi-chromosome, multi-chromosome evolutionary strategy, evolution, digital circuits, cartesian genetic programming, module acquisition |
| 1 | Rachel Cavill, Stephen L. Smith, Andy M. Tyrrell |
Variable length genetic algorithms with multiple chromosomes on a variant of the Onemax problem. |
GECCO |
2006 |
DBLP DOI BibTeX RDF |
genetic algorithms, representations, size |
| 1 | Rachel Cavill, Stephen L. Smith, Andrew M. Tyrrell |
Multi-chromosomal genetic programming. |
GECCO |
2005 |
DBLP DOI BibTeX RDF |
team evolution, genetic programming, representations |
| 1 | Rachel Cavill, Stephen L. Smith, Andy Terrell |
The performance of polyploid evolutionary algorithms is improved both by having many chromosomes and by having many copies of each chromosome on symbolic regression problems. |
Congress on Evolutionary Computation |
2005 |
DBLP DOI BibTeX RDF |
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