Abstract
A dynamic Optimisation Problem with Unknown Active Variables (DOPUAV) is a dynamic problem in which the activity of the variables changes as time passes, to simulate the dynamicity in the problem’s variables. In this paper, several variations of genetic algorithms are proposed to solve DOPUAV. They are called Detectable techniques. These techniques try to detect where the problem changes, before detecting the active variables. These variations are tested, then the best variation is compared with the best previously used algorithms namely Hyper Mutation (HyperM), Random Immigration GA (RIGA), as well as simple GA (SGA). The results and statistical analysis show the superiority of our proposed algorithm.
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AbdAllah, A.F.M., Essam, D.L., Sarker, R.A. (2017). Detectable Genetic Algorithms-Based Techniques for Solving Dynamic Optimisation Problem with Unknown Active Variables. In: Wagner, M., Li, X., Hendtlass, T. (eds) Artificial Life and Computational Intelligence. ACALCI 2017. Lecture Notes in Computer Science(), vol 10142. Springer, Cham. https://doi.org/10.1007/978-3-319-51691-2_19
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DOI: https://doi.org/10.1007/978-3-319-51691-2_19
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