Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Neural networks and the bias/variance dilemma
Neural Computation
Efficiently representing populations in genetic programming
Advances in genetic programming
Type inheritance in strongly typed genetic programming
Advances in genetic programming
On using syntactic constraints with genetic programming
Advances in genetic programming
Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Genetic Programming, Ensemble Methods and the Bias/Variance Tradeoff - Introductory Investigations
Proceedings of the European Conference on Genetic Programming
Ripple Crossover in Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Grammatical bias for evolutionary learning
Grammatical bias for evolutionary learning
Strongly typed genetic programming
Evolutionary Computation
Evolutionary program induction directed by logic grammars
Evolutionary Computation
Search bias, language bias and genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Prediction and Modelling of the Flow of a Typical Urban Basin through Genetic Programming
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Determining equations for vegetation induced resistance using genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The estimation of hölderian regularity using genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A machine code-based genetic programming for suspended sediment concentration estimation
Advances in Engineering Software
Forecasting monthly urban water demand using Extended Kalman Filter and Genetic Programming
Expert Systems with Applications: An International Journal
Evolving estimators of the pointwise Hölder exponent with Genetic Programming
Information Sciences: an International Journal
Hi-index | 0.00 |
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.