Abstract
This paper proposes two general techniques for adapting operators in a genetic algorithm: one dynamically adjusts their rates, while the other customizes their specific way of operation. We show how these techniques can be integrated into a single evolutionary system, called Integrated-Adaptive Genetic Algorithm (IAGA). The IAGA exhibits fewer input parameters to adjust than the original GA, while being able to automatically adapt itself to the particularities of the optimization problem it tackles. We present a proof-of-concept implementation of this technique for royal road functions.
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© 2003 Springer-Verlag Berlin Heidelberg
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Luchian, H., Gheorghieş, O. (2003). Integrated-Adaptive Genetic Algorithms. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds) Advances in Artificial Life. ECAL 2003. Lecture Notes in Computer Science(), vol 2801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39432-7_68
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DOI: https://doi.org/10.1007/978-3-540-39432-7_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20057-4
Online ISBN: 978-3-540-39432-7
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