Abstract
Genetic algorithms (GA) represent an algorithmic optimization technique inspired by biological evolution. A major strength of this meta-heuristic is its ability to explore the search space in independent parallel search routes rendering the algorithm highly efficient if implemented on a parallel architecture. Sequential simulations of GAs frequently result in enormous computational costs. To alleviate this problem, we propose a serial evolution strategy which results in a much smaller number of necessary fitness function evaluations thereby speeding up the computation considerably. If implemented on a parallel architecture the savings in computational costs are even more pronounced. We present the algorithm in full mathematical detail and proof the corresponding schema theorem for a simple case without cross-over operations. A toy example illustrates the operation of serial evolution and the performance improvement over a canonical genetic algorithm.
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© 2009 Springer-Verlag Berlin Heidelberg
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Fischer, V., Tomé, A.M., Lang, E.W. (2009). Serial Evolution. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds) Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy. IWINAC 2009. Lecture Notes in Computer Science, vol 5601. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02264-7_25
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DOI: https://doi.org/10.1007/978-3-642-02264-7_25
Publisher Name: Springer, Berlin, Heidelberg
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