Scalable, Cellular Architectures, for Improving Solutions With Experience, in Combinatorially Complex Optimization Problems

可扩展的细胞结构，以改善解决方案的经验，在组合复杂的优化问题这个项目将解决的一般任务，解决优化问题的标准是一个非常复杂的，非凸函数的选择提供给用户。作为一个测试平台的例子，它将集中在经典的旅行推销员问题（TSP），许多实际应用和基准。通用的学习系统从来没有能够执行以及特设的，具体问题的方法在这里;然而，通过使用新的学习方法，这个PI已经取得了比以前的努力，这类更好的结果。在这个项目中，他计划走得更远，通过使用更复杂的神经网络设计，这些设计是由最近在智能控制和强化学习方面的工作所激发的。 从长远来看，更好的基于学习的优化工具将是重要的，因为它是基于学习的类脑系统的一部分，可以随着时间的推移做出决策，并管理复杂的网络系统。该项目还将包括与K-12和EPSCOR机构的合作，并将为本科生生成顶点工程项目。

Scalable Cellular Architectures for Improving Solutions with Experience inCombinatorially Complex Optimization ProblemsThis project will address the general task of solving optimization problems where the criterion to be optimized is a very complicated, nonconvex function of the choices available to the user. As a testbed example, it will focus on the classic Traveling Salesman Problem (TSP) for which many practical applications and benchmarks are available. General-purpose learning systems have never been able to perform as well as ad hoc, problem-specific methods here; however, by using new learning approaches, this PI has already achieved results much better than previous efforts of that type. In this project, he plans to go further, by using more sophisticated neural network designs motivated by recent work in intelligent control and reinforcement learning. Better tools for learning-based optimization will be important in the long-term as part of learning-based brain-like systems to make decisions over time and to managecomplex network systems.The project will also include collaboration with K-12 and EPSCOR institutions, and will generate capstone engineering projects for undergraduate students.