Anzeige
Erschienen in:
2008 | OriginalPaper | Buchkapitel
Exposing a Bias Toward Short-Length Numbers in Grammatical Evolution
verfasst von : Marco A. Montes de Oca
Erschienen in: Genetic Programming
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Many automatically-synthesized programs have, like their hand-made counterparts, numerical parameters that need to be set properly before they can show an acceptable performance. Hence, any approach to the automatic synthesis of programs needs the ability to tune numerical parameters efficiently.
Grammatical Evolution (GE) is a promising grammar-based genetic programming technique that synthesizes numbers by concatenating digits. In this paper, we show that a naive application of this approach can lead to a serious number length bias that in turn affects efficiency. The root of the problem is the way the context-free grammar used by GE is defined. A simple, yet effective, solution to this problem is proposed.