A comparison of linear genetic programming and neural networks in medical data mining

By Markus Brameier, Wolfgang Banzhaf. IEEE Transactions on Evolutionary Computation, 2001

535 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/eae252e05061c283668e1eb2d03e1b6169c17f72

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Citations in the corpus

Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, background): “An example of linear genetic programming applied to medical classification problems from a benchmark database compared with results obtained by neural networks can be found in [42].”
Gene-Expression Programming for the Development of a Stage-Discharge Curve of the Pahang River (2011, background): “Each function implicitly includes an assignment to a variable, which facilitates the use of multiple program outputs in GP, whereas in a tree-based GP, these side effects must be incorporated explicitly ( Brameier and Banzhaf 2001 ).”
A Survey on the Application of Genetic Programming to Classification (2010, methodology): “GP as a side effect [23], [63], [90], [93], [99], [102], [106].”
Energy-based numerical models for assessment of soil liquefaction (2012, background): “2b) (Brameier and Banzhaf, 2001, 2007).”
Global Optimization Algorithms -- Theory and Application (2009, result): “The area of Genetic Programming that works with such instruction string genomes is called linear Genetic Programming (LGP) [838, 839, 840, 841, 842, 843, 746] in contrast to the traditional tree-based Genetic Programming (TGP).”
Cartesian Genetic Programming (2015, background): “The possibility of improving efﬁciency by stripping out non-coding instructions prior to phenotype evaluation has also been suggested for Linear GP [1].”
Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare (2015, background): “These applications include healthcare [1], network intrusion detection systems[2], spam and fraud detection, phishing detection [3], political decision making [4], adversarial advertisement detection [5], and financial engineering [6].”