What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Illustrating evolutionary computation with Mathematica
Illustrating evolutionary computation with Mathematica
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
GeLog - A System Combining Genetic Algorithm with Inductive Logic Programming
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
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This article introduces modifications that have been applied to GeLog, a genetic logic programming framework, in order to improve its performance. The main emphasis of this work is the structure processing of genetic algorithms. As studies have shown, the linkage of genes plays an important role in the performance of genetic algorithms. Thus, different approaches that take linkage learning into account have been reviewed and the most promising has been implemented and tested with GeLog. It is demonstrated that the modified program solves problems that proved hard for the original system.