- author = "Maarten Keijzer and Vladan Babovic",
- title = "Declarative and Preferential Bias in {GP}-based Scientific Discovery",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2002",
- volume = "3",
- number = "1",
- pages = "41--79",
- month = mar,
- keywords = "genetic algorithms, genetic programming, symbolic regression, strong typing, coercion typing, empirical equations, hydraulics",
- ISSN = "1389-2576",
-
URL = "
https://rdcu.be/czchJ",
-
DOI = "
doi:10.1023/A:1014596120381",
- size = "39 pages",
-
abstract = "This work examines two methods for evolving
dimensionally correct equations on the basis of data.
It is demonstrated that the use of units of measurement
aids in evolving equations that are amenable to
interpretation by domain specialists. One method uses a
strong typing approach that implements a declarative
bias towards correct equations, the other method uses a
coercion mechanism in order to implement a preferential
bias towards the same objective. Four experiments using
real-world, unsolved scientific problems were performed
in order to examine the differences between the
approaches and to judge the worth of the induction
methods.
Not only does the coercion approach perform significantly better on two out of the four problems when compared to the strongly typed approach, but it also regularizes the expressions it induces, resulting in a more reliable search process.
A trade-off between type correctness and ability to solve the problem is identified. Due to the preferential bias implemented in the coercion approach, this trade-off does not lead to sub-optimal performance. No evidence is found that the reduction of the search space achieved through declarative bias helps in finding better solutions faster. In fact, for the class of scientific discovery problems the opposite seems to be the case.",
- notes = "Article ID: 395989",
