Scaling Up Inductive Logic Programming: An Evolutionary Wrapper Approach
Applied Intelligence
Evolutionary approaches to fuzzy modelling for classification
The Knowledge Engineering Review
Evolutionary program induction directed by logic grammars
Evolutionary Computation
Learning recursive functions from noisy examples using generic genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Semantics based crossover for boolean problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Semantic similarity based crossover in GP: the case for real-valued function regression
EA'09 Proceedings of the 9th international conference on Artificial evolution
The role of syntactic and semantic locality of crossover in genetic programming
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Semantically-based crossover in genetic programming: application to real-valued symbolic regression
Genetic Programming and Evolvable Machines
Examining the landscape of semantic similarity based mutation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Structural difficulty in estimation of distribution genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Improving the generalisation ability of genetic programming with semantic similarity based crossover
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
On the roles of semantic locality of crossover in genetic programming
Information Sciences: an International Journal
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Genetic Programming (GP) and Inductive Logic Programming (ILP) have received increasing interest recently. Since their formalisms are so different, these two approaches cannot be integrated easily though they share many common goals and functionalities. A unification will greatly enhance their problem solving power. Moreover, they are restricted in the computer languages in which programs can be induced. In this paper, we have presented a flexible system called LOGENPRO (LOgic grammar based GENetic PROgramming) that combines GP and ILP. It is based on a formalism of logic grammars. The system can learn programs in various programming languages and represent context-sensitive information and domain-dependent knowledge. The performance of LOGENPRO in inducing logic programs from noisy examples is evaluated. A detailed comparison to FOIL has been conducted. This experiment demonstrates that LOGENPRO is a promising alternative to other inductive logic programming systems and sometimes is superior for handling noisy data. Moreover, a series of examples are used to illustrate that LOGENPRO is so flexible that programs in different programming languages including LISP, Prolog and Fuzzy Prolog. can be induced.