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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Fischer, V., Tomé, A. M., & Lang, E. W. (2009). Serial evolution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5601 LNCS, pp. 233–244). https://doi.org/10.1007/978-3-642-02264-7_25
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