用智能坐标改进搜索算法
Improving Search Algorithms by Using Intelligent Coordinates
摘要：
我们考虑的问题，设计一组计算代理，使他们都追求自己的利益，一个全局函数G的集体系统的优化。三个因素决定了这种设计的质量。第一种涉及用于找到这样的全局函数的最大值的常规探索-利用搜索算法，例如，模拟退火博弈论算法与第二个因素有关，第三个因素与机器学习领域的技术有关。在这里，我们演示了如何利用所有三个因素，通过修改搜索算法的探索阶段，而不是通过随机抽样，每个坐标的基础搜索空间是由一个相关的基于机器学习的"玩家“从事非合作对策。实验表明，这种修改提高了SA的数量级的装箱和一个模型的经济过程中运行的底层网络。这些实验还揭示了新的小世界现象。
英文摘要：
We consider the problem of designing a set of computational agents so that as they all pursue their self-interests a global function G of the collective system is optimized. Three factors govern the quality of such design. The first relates to conventional exploration-exploitation search algorithms for finding the maxima of such a global function, e.g., simulated annealing. Game-theoretic algorithms instead are related to the second of those factors, and the third is related to techniques from the field of machine learning. Here we demonstrate how to exploit all three factors by modifying the search algorithm's exploration stage so that rather than by random sampling, each coordinate of the underlying search space is controlled by an associated machine-learning-based ``player'' engaged in a non-cooperative game. Experiments demonstrate that this modification improves SA by up to an order of magnitude for bin-packing and for a model of an economic process run over an underlying network. These experiments also reveal novel small worlds phenomena.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
