Multi-objective Random Bit Climbers with Weighted Permutation on Large Scale Binary MNK-Landscapes
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70085-9_11
Reference32 articles.
1. Aguirre, H., Oyama, A., Tanaka, K.: Adaptive $$\epsilon $$-sampling and $$\epsilon $$-hood for evolutionary many-objective optimization. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds.) EMO 2013. LNCS, vol. 7811, pp. 322–336. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37140-0_26
2. Aguirre, H., Tanaka, K.: Insights on properties of multiobjective mnk-landscapes. In: Proceedings of the 2004 Congress on Evolutionary Computation, vol. 1, pp. 196–203 (2004). https://doi.org/10.1109/CEC.2004.1330857
3. Aguirre, H., Tanaka, K.: Random bit climbers on multiobjective mnk-landscapes: Effects of memory and population climbing. IEICE Trans. 88-A, 334–345 (2005). https://doi.org/10.1093/ietfec/E88-A.1.334
4. Aguirre, H., Tanaka, K.: Working principles, behavior, and performance of moeas on mnk-landscapes. Eur. J. Oper. Res. 181(3), 1670–1690 (2007). https://doi.org/10.1016/j.ejor.2006.08.004
5. Lecture Notes in Computer Science;H Aguirre,2016
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