Abstract
This paper presents a principled way of designing a genetic algorithm which can guarantee a rigorously proven upper bound on its optimization time. The shortest path problem is selected to demonstrate how level-based analysis, a general purpose analytical tool, can be used as a design guide. We show that level-based analysis can also ease the experimental burden of finding appropriate parameter settings. Apart from providing an example of theory-driven algorithmic design, we also provide the first runtime analysis of a non-elitist population-based evolutionary algorithm for both the single-source and all-pairs shortest path problems.
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Acknowledgements
This research received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no 618091 (SAGE) and from the EPSRC under grant agreement no EP/M004252/1.
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Corus, D., Lehre, P.K. (2018). Theory Driven Design of Efficient Genetic Algorithms for a Classical Graph Problem. In: Amodeo, L., Talbi, EG., Yalaoui, F. (eds) Recent Developments in Metaheuristics. Operations Research/Computer Science Interfaces Series, vol 62. Springer, Cham. https://doi.org/10.1007/978-3-319-58253-5_8
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DOI: https://doi.org/10.1007/978-3-319-58253-5_8
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