Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
A Knowledge-Intensive Genetic Algorithm for Supervised Learning
Machine Learning - Special issue on genetic algorithms
Parallel Genetic Algorithms: Theory and Applications
Parallel Genetic Algorithms: Theory and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Search-intensive concept induction
Evolutionary Computation
Evolutionary program induction directed by logic grammars
Evolutionary Computation
Flexible learning of problem solving heuristics through adaptive search
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
QG/GA: a stochastic search for Progol
Machine Learning
Improving inductive logic programming by using simulated annealing
Information Sciences: an International Journal
QG/GA: A Stochastic Search for Progol
Inductive Logic Programming
Hybrid Learning of Ontology Classes
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Facts and fallacies in using genetic algorithms for learning clauses in first-order logic
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Context-sensitive refinements for stochastic optimisation algorithms in inductive logic programming
Artificial Intelligence Review
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Learning with configurable operators and RL-based heuristics
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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In a previous paper we introduced a framework for combining Genetic Algorithms with ILP which included a novel representation for clauses and relevant operators. In this paper we complete the proposed framework by introducing a fast evaluation mechanism. In this evaluation mechanism individuals can be evaluated at genotype level (i.e. bit-strings) without mapping them into corresponding clauses. This is intended to replace the complex task of evaluating clauses (which usually needs repeated theorem proving) with simple bitwise operations. In this paper we also provide an experimental evaluation of the proposed framework. The results suggest that this framework could lead to significantly increased efficiency in problems involving complex target theories.