摘要翻译：
由于自由能结构极其崎岖，自旋玻璃基态的确定是已知最难的优化问题之一，在最一般的情况下被发现是NP难的。由于局域（自由）能量极小值的特殊结构，通用的优化策略在这些问题上的效果相对较差，许多特别定制的优化技术已经发展起来，尤其是针对伊辛自旋玻璃和类似的离散系统。本文介绍了一种有效的优化启发式算法，该算法针对较少讨论的连续旋转情况，将Ising旋转嵌入到连续旋转器中，并结合了适当的遗传算法变体。讨论了在寻找（数值）精确基态时确保高可靠性的统计技术，并将该方法与模拟退火方法进行了比较。
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英文标题：
《Genetic embedded matching approach to ground states in continuous-spin
  systems》
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作者：
Martin Weigel
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最新提交年份：
2007
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分类信息：

一级分类：Physics        物理学
二级分类：Disordered Systems and Neural Networks        无序系统与神经网络
分类描述：Glasses and spin glasses; properties of random, aperiodic and quasiperiodic systems; transport in disordered media; localization; phenomena mediated by defects and disorder; neural networks
眼镜和旋转眼镜；随机、非周期和准周期系统的性质；无序介质中的传输；本地化；由缺陷和无序介导的现象；神经网络
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一级分类：Physics        物理学
二级分类：Statistical Mechanics        统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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英文摘要：
  Due to an extremely rugged structure of the free energy landscape, the determination of spin-glass ground states is among the hardest known optimization problems, found to be NP-hard in the most general case. Owing to the specific structure of local (free) energy minima, general-purpose optimization strategies perform relatively poorly on these problems, and a number of specially tailored optimization techniques have been developed in particular for the Ising spin glass and similar discrete systems. Here, an efficient optimization heuristic for the much less discussed case of continuous spins is introduced, based on the combination of an embedding of Ising spins into the continuous rotators and an appropriate variant of a genetic algorithm. Statistical techniques for insuring high reliability in finding (numerically) exact ground states are discussed, and the method is benchmarked against the simulated annealing approach.
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PDF链接：
https://arxiv.org/pdf/706.4408