Communications of the ACM
Machine learning of inductive bias
Machine learning of inductive bias
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The Utility of Knowledge in Inductive Learning
Machine Learning
On using syntactic constraints with genetic programming
Advances in genetic programming
Learning Logical Definitions from Relations
Machine Learning
Scaling Up Inductive Logic Programming: An Evolutionary Wrapper Approach
Applied Intelligence
Declarative and Preferential Bias in GP-based Scientific Discovery
Genetic Programming and Evolvable Machines
Some Experimental Results with Tree Adjunct Grammar Guided Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
General schema theory for genetic programming with subtree-swapping crossover: Part II
Evolutionary Computation
Evolving rule-based systems in two medical domains using genetic programming
Artificial Intelligence in Medicine
Learning to Solve Planning Problems Efficiently by Means of Genetic Programming
Evolutionary Computation
Evolutionary program induction directed by logic grammars
Evolutionary Computation
Schema theory for genetic programming with one-point crossover and point mutation
Evolutionary Computation
Scaling of program fitness spaces
Evolutionary Computation
Evolving Regular Expressions for GeneChip Probe Performance Prediction
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Semantic analysis of program initialisation in genetic programming
Genetic Programming and Evolvable Machines
A comparison of classification accuracy of four genetic programming-evolved intelligent structures
Information Sciences: an International Journal
Tree adjoining grammars, language bias, and genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Cost-benefit investigation of a genetic-programming hyperheuristic
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Semantic bias in program coevolution
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
| Hi-index | 0.00 |
The use of bias with automated learning systems has become an important area of research. The use of bias with evolutionary techniques of learning has been shown to have advantages when complex structures are evolved. This is especially true when the semantics of the evolving population of structures is not explicitly represented or analysed during the evolution. This paper describes a framework which brings together two types of bias, namely search bias (the way new structures are created) and language bias (the form of possible structures that may be created). The resulting system extends genetic programming (GP) by allowing declarative bias with both the form of possible solutions that are created and the method by which they are transformed.