- author = "James V. Hansen",
- title = "Genetic Programming Experiments with Standard and Homologous Crossover Methods",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2003",
- volume = "4",
- number = "1",
- pages = "53--66",
- month = mar,
- keywords = "genetic algorithms, genetic programming, homologous crossover, regression, classifications",
- ISSN = "1389-2576",
-
DOI = "
doi:10.1023/A:1021825110329",
- size = "14 pages",
-
abstract = "While successful applications have been reported using
standard GP crossover, limitations of this approach
have been identified by several investigators. Among
the most compelling alternatives to standard GP
crossover are those that use some form of homologous
crossover, where code segments that are exchanged are
structurally or syntactically aligned in order to
preserve context and worth. This paper reports the
results of an empirical comparison of GP using standard
crossover methods with GP using homologous crossover
methods. Ten problems are tested, five each of pattern
recognition and regression.
Results suggest that in terms of generalisation accuracy, homologous crossover does generate consistently better performance. In addition, there is a consistently lower fraction of introns that are generated in the solution code.",
- notes = "Article ID: 5113072",
