title = "Crossover and mutation operators for grammar-guided
genetic programming",
journal = "Soft Computing",
year = "2007",
volume = "11",
number = "10",
pages = "943--955",
month = aug,
keywords = "genetic algorithms, genetic programming,
Grammar-guided genetic programming, Crossover,
Mutation, Breast cancer prognosis",
DOI = "doi:10.1007/s00500-006-0144-9",
abstract = "This paper proposes a new grammar-guided genetic
programming (GGGP) system by introducing two original
genetic operators: crossover and mutation, which most
influence the evolution process. The first, the
so-called grammar-based crossover operator, strikes a
good balance between search space exploration and
exploitation capabilities and, therefore, enhances GGGP
system performance. And the second is a grammar-based
mutation operator, based on the crossover, which has
been designed to generate individuals that match the
syntactical constraints of the context-free grammar
that defines the programs to be handled. The use of
these operators together in the same GGGP system
assures a higher convergence speed and less likelihood
of getting trapped in local optima than other related
approaches. These features are shown throughout the
comparison of the results achieved by the proposed
system with other important crossover and mutation
methods in two experiments: a laboratory problem and
the real-world task of breast cancer prognosis.",
notes = "p945 'ambiguous' context free grammars. p950 PCT2 SSGA
75percent crossover 5percent mutation. p952 315 breast
lesions X-ray images characteristics by human: size
(apparent diameter mm), morphology (5 values), margins
(5 values), density (4 values). Biopsy as ground truth,
Comparison with two human experts. Evolved rule: if
margins=spiculated and morphology=irregular then
prognosis=malignant.