Abstract
In evolutionary computation, inferior solutions are often discarded to expedite convergence, potentially bypassing valuable optimization insights. To rectify this, we have developed a novel method, drawing inspiration from ground state evolution (GSE) and quantum annealing (QA). These processes retain numerous positions with non-minimum potential energy, enabling the construction of the lowest possible energy wave function. Our method’s theoretical foundation is meticulously explained through the quantum path integral. We have realized this theory via numerical simulation, utilizing population-based evolution driven by multi-scale Gaussian sampling with a decreasing scale, mimicking QA with multi-scale diffusion Monte Carlo (DMC). A series of rigorous experiments highlight the unique attributes and effectiveness of this method. Importantly, our approach generates a vast array of inferior solutions consistently. Their distribution indicates regions of lower function values within the solution space, presenting a new perspective on the utilization of inferior solutions. The implications of this research promise enhancements in solving optimization problems, potentially improving efficiency in evolutionary computation and beyond.
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Yang, G., Wang, P., Xin, G., Yin, X. (2023). A Quantum Simulation Method with Repeatable Steady-State Output Using Massive Inferior Solutions. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14086. Springer, Singapore. https://doi.org/10.1007/978-981-99-4755-3_58
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DOI: https://doi.org/10.1007/978-981-99-4755-3_58
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