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

结合组合复杂优化问题的经验改进解决方案的可扩展蜂窝体系结构本项目将解决解决优化问题的一般任务，其中要优化的准则是用户可用选择的非常复杂的非凸函数。作为一个试验床的例子，它将集中在经典的旅行商问题(TSP)，其中有许多实际应用程序和基准。通用学习系统在这里从来不能像特别的、针对问题的方法那样表现良好；然而，通过使用新的学习方法，这次PI已经取得了比以前那种类型的努力更好的结果。在这个项目中，他计划走得更远，使用更复杂的神经网络设计，这是受到最近智能控制和强化学习工作的推动。从长远来看，作为基于学习的类大脑系统的一部分，更好的基于学习的优化工具将是重要的，以便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，并将为本科生产生顶峰工程项目。

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.