Optimal mutation and crossover rates for a genetic algorithm operating in a dynamic environment (original) (raw)

Abstract

We attempt to find mutation / crossover rate pairs that facilitate the performance of a genetic algorithm (GA) on a simple dynamic fitness function. This research results in two products. The first is a dynamic fitness function that is founded in previous analysis done on both static and dynamic landscapes, and that avoids problematic issues with previously proposed dynamic landscapes for GAs. The second is a general relationship between the crossover and mutation rates that are most useful for a dynamic fitness function with a specific rate of change in Hamming distance, and that could possibly provide insight into the utility of the standard GA approach for the optimization of dynamic landscapes.

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Authors and Affiliations

1. Advanced Information Systems Group, ERIM International, P.O. Box 134008, 48113-4008, Ann Arbor, Michigan, USA
   Stephen A. Stanhope
2. Artificial Intelligence & Space Physics Research Laboratories, The University of Michigan, 2455 Hayward Avenue, 48113-4008, Ann Arbor, Michigan, USA
   Jason M. Daida

Authors

1. Stephen A. Stanhope
2. Jason M. Daida

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V. W. Porto N. Saravanan D. Waagen A. E. Eiben

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© 1998 Springer-Verlag Berlin Heidelberg

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Stanhope, S.A., Daida, J.M. (1998). Optimal mutation and crossover rates for a genetic algorithm operating in a dynamic environment. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040820

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• DOI: https://doi.org/10.1007/BFb0040820
• Published: 10 December 2005
• Publisher Name: Springer, Berlin, Heidelberg
• Print ISBN: 978-3-540-64891-8
• Online ISBN: 978-3-540-68515-9
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