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- W2153197624 abstract "development in the field of quantum computing gives us a significant edge over classical computing in terms of time and efficiency. This is particularly useful for NP-hard problems such as graph layout problems. Since many real world problems are effectively solved by genetic algorithm (GA) and the performance of GA highly depends upon the setting of its parameters, therefore this paper focuses on a Quantum Inspired Genetic Algorithm (QIGA) and develops and evaluates adaptive strategies for the same. QIGA adapts ideas of Q-bits, superposition of Q-bits from quantum computing. The effectiveness and the applicability of adaptive QIGA is demonstrated by experimental results on the benchmark Knapsack, Maxcut and Onemax combinatorial optimization problems. The results show that adaptive QIGA is superior to QIGAs. Keywordsinspired genetic algorithm, Parameter control, adaptive QIGA." @default.
- W2153197624 created "2016-06-24" @default.
- W2153197624 creator A5044293858 @default.
- W2153197624 date "2014-12-18" @default.
- W2153197624 modified "2023-10-01" @default.
- W2153197624 title "Adaptive Quantum Inspired Genetic Algorithm for Combinatorial Optimization Problems" @default.
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- W2153197624 doi "https://doi.org/10.5120/18743-9996" @default.
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