Abstract
Population-based meta-heuristics can admit population models and neighborhood topologies, which have a significant influence on their performance. Quantum-inspired evolutionary algorithms (QEA) often use coarse-grained population model and have been successful in solving difficult search and optimization problems. However, it was recently shown that the performance of QEA can be improved by changing its population model and neighborhood topologies. This paper investigates the effect of static spatial topologies on the performance of QEA with fine-grained population model on well-known benchmark problem generator known as P-PEAKS.
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Mani, N., Saran, G., Mani, A. (2019). Performance of Static Spatial Topologies in Fine-Grained QEA on a P-PEAKS Problem Instance. In: Kapur, P., Klochkov, Y., Verma, A., Singh, G. (eds) System Performance and Management Analytics. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7323-6_16
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DOI: https://doi.org/10.1007/978-981-10-7323-6_16
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