Intelligent Exploration for Genetic Algorithms
Using Self-Organizing Maps in Evolutionary Computation
(Sprache: Englisch)
Exploration vs. exploitation is a well known issue in Evolutionary Algorithms. Accordingly, an unbalanced search can lead to premature convergence. GASOM, a novel Genetic Algorithm, addresses this problem by intelligent exploration techniques. The approach...
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Exploration vs. exploitation is a well known issue in Evolutionary Algorithms. Accordingly, an unbalanced search can lead to premature convergence. GASOM, a novel Genetic Algorithm, addresses this problem by intelligent exploration techniques. The approach uses Self-Organizing Maps to mine data from the evolution process. The information obtained is successfully utilized to enhance the search strategy and confront genetic drift. This way, local optima are avoided and exploratory power is maintained. The evaluation of GASOM on well known problems shows that it effectively prevents premature convergence and seeks the global optimum. Particularly in deceptive and missleading functions it showed outstanding performance. Additionally, representing the search history by the Self-Organizing Map provides a visually pleasing insight into the state and course of evolution.
Autoren-Porträt von Heni Ben Amor, Heni B. Amor
Ben Amor, HeniHeni Ben Amor, Dipl. Inform.: Graduated in Computational Visualistics from the University of Koblenz-Landau in Germany. External collaborator at the Intelligent Robotics Lab in Japan and PhD. Student at the Technical University Bergakademie Freiberg.
Bibliographische Angaben
- Autoren: Heni Ben Amor , Heni B. Amor
- 2008, 76 Seiten, Masse: 15 x 22 cm, Kartoniert (TB), Englisch
- Verlag: VDM Verlag Dr. Müller e.K.
- ISBN-10: 3836488639
- ISBN-13: 9783836488631
Sprache:
Englisch
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