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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 baseddoi:10.1007/0-387-29295-0_44 dblp:conf/ifip12/SueyiKS05 fatcat:suhiak2i3vhkrkozq57vq4fwmm