Abstract
Genetic Algorithms (GAs) are a robust heuristic search technique capable of taking on a broad range of optimization problems. In most GAs, components and parameters are predetermined and remain static throughout its run. In this paper, it is hypothesized that a GA’s performance and robustness can be enhanced through the ‘online’ adaptation of the operators and an operator based adaptive genetic algorithm (AGA) based on these concepts is designed and implemented. A number of permutation based problems were selected to evaluate the performance of AGA.
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Sueyi, K., Kar, L., Seng, L.K. (2005). An Operator Based Adaptive Genetic Algorithm. In: Li, D., Wang, B. (eds) Artificial Intelligence Applications and Innovations. AIAI 2005. IFIP — The International Federation for Information Processing, vol 187. Springer, Boston, MA. https://doi.org/10.1007/0-387-29295-0_44
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DOI: https://doi.org/10.1007/0-387-29295-0_44
Publisher Name: Springer, Boston, MA
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