Foundations of logic programming
Foundations of logic programming
Experimental comparison of human and machine learning formalisms
Proceedings of the sixth international workshop on Machine learning
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
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 evolution of strategies for multiagent environments
Adaptive Behavior
Genetic and evolutionary algorithms come of age
Communications of the ACM
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
Inductive logic programming: derivations, successes and shortcomings
ACM SIGART Bulletin
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Optimization with genetic algorithm hybrids that use local searches
Adaptive individuals in evolving populations
Evolving recursive functions for the even-parity problem using genetic programming
Advances in genetic programming
Type inheritance in strongly typed genetic programming
Advances in genetic programming
On using syntactic constraints with genetic programming
Advances in genetic programming
Time series modeling using genetic programming: an application to rainfall-runoff models
Advances in genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A System for Learning Control Strategies with Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Using Genetic Algorithms to Solve NP-Complete Problems
Proceedings of the 3rd International Conference on Genetic Algorithms
Cooperative Concept Learning By Means Of A Distributed GA
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Learning Decision Strategies with Genetic Algorithms
AII '92 Proceedings of the International Workshop on Analogical and Inductive Inference
Part-of-Speech Tagging Using Progol
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Research of Mobile Agent Based Network Topology Discovery
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
Search bias, language bias and genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Evolutionary computation: a unified approach
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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Inductive logic programming (ILP) algorithms are classification algorithms that construct classifiers represented as logic programs. ILP algorithms have a number of attractive features, notably the ability to make use of declarative background (user-supplied) knowledge. However, ILP algorithms deal poorly with large data sets (104 examples) and their widespread use of the greedy set-covering algorithm renders them susceptible to local maxima in the space of logic programs.This paper presents a novel approach to address these problems based on combining the local search properties of an inductive logic programming algorithm with the global search properties of an evolutionary algorithm. The proposed algorithm may be viewed as an evolutionary wrapper around a population of ILP algorithms.The evolutionary wrapper approach is evaluated on two domains. The chess-endgame (KRK) problem is an artificial domain that is a widely used benchmark in inductive logic programming, and Part-of-Speech Tagging is a real-world problem from the field of Natural Language Processing. In the latter domain, data originates from excerpts of the Wall Street Journal. Results indicate that significant improvements in predictive accuracy can be achieved over a conventional ILP approach when data is plentiful and noisy.