Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Proceedings of the 8th International Workshop on Inductive Logic Programming
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Proceedings of the 10th International Conference on Inductive Logic Programming
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Which Hypotheses Can Be Found with Inverse Entailment?
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Learning from good data and bad
Learning from good data and bad
A genetic algorithms approach to ILP
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
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Over the last few years, a few approaches have been proposed aiming to combine genetic and evolutionary computation (GECCO) with inductive logic programming (ILP). The underlying rationale is that evolutionary algorithms, such as genetic algorithms, might mitigate the combinatorial explosions generated by the inductive learning of rich representations, such as those used in first-order logic. Particularly, the binary representation approach presented by Tamaddoni-Nezhad and Muggleton has attracted the attention of both the GECCO and ILP communities in recent years. Unfortunately, a series of systematic and fundamental theoretical errors renders their framework moot. This paper critically examines the fallacious claims in the mentioned approach. It is shown that, far from restoring completeness to the learner progol's search of the subsumption lattice, the binary representation approach is both overwhelmingly unsound and severely incomplete.