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. © 2005 by International Federation for Information Processing.
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CITATION STYLE
Sueyi, K., Kar, L., & Seng, L. K. (2005). An operator based adaptive genetic algorithm. In IFIP Advances in Information and Communication Technology (Vol. 187, pp. 415–424). Springer New York LLC. https://doi.org/10.1007/0-387-29295-0_44
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