# Designing and Implementing the transformation, fitness and selectionmechanism in a learning system combining ILP and GA

Student: | Knut Sander |
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Title: | Designing and Implementing the transformation, fitness and selectionmechanism in a learning system combining ILP and GA |

Type: | diploma thesis |

Advisors: | Kókai, G.; Fischer, I.; Schneider, H. |

State: | submitted on November 10, 2000 |

Prerequisits: | Systems that induce first-order logic programs have drawn considerable interest recently within the artificial intelligence community. Inductive logic programming (ILP), for example, has very impressive applications in knowledge discovery in databases. Genetic programming (GP), a promising alternative that builds on genetic algorithm search strategies, demonstrates equally impressive results across a wide range of uses. Genetic logic programming (GLP) was apparentaly first suggested from Whigham as an alternative approach to Inductive Logic Programming. Lappoon et al. also implemented a version of GLP for inducing Prolog programs but his experiments shows that to compare the performance of ILP, GP and GLP on four easy list examples the performance of GLP was the worst; it did not produce a completely correct program in any of the trials. Wong and Kwong developed Programming Structure, that also integrates these two better known approaches. To demonstrate the viability of their GLPS approach, they have tested a preliminary implementation on a battery of learning tasks: Winston's arch problem, the modified Quinlan network reachability problem, the factorial problem, and the chess endgame problem |

Topic: | Implementing the transformation of the basic data structure into Prolog clauses and vice versa. |