Symbiotic Evolution of Neural Networks in Sequential Decision Tasks

顺序决策任务出现在许多现实世界的领域，包括控制，资源分配，路由和调度。 本计画的目标是发展一种新的方法，以神经网路的共生演化为基础来进行序贯决策。 在共生进化中，一群神经元 与 遗传算法 合作 和 形成决策网络。 多样性得以保持 在 人口作为任务的一部分，即使在困难的任务中，系统也可以有效地找到好的解决方案， 稀疏增强。在拟议的项目中，将从理论和实验两方面分析共生进化，该算法将进一步开发并应用于局域网管理等复杂的现实领域，并将开发一个实用的高级接口，以允许快速应用该方法到新领域。 该研究的主要科学贡献预计是：（1）一种新颖的，强大的方法，用于自动提取和编码特定问题的知识，用于顺序决策，（2）对多样性和合作的作用的透彻理解， 遗传 算法 的发展 的 通用的应用接口也将使控制工程，军事科学，作战管理等许多实际领域受益。 主要的假设是：（1） 神经网络的模式识别和泛化能力可以用来实现有效和鲁棒的顺序决策策略：（2）遗传算法即使在稀疏强化下也能发现强有力的问题特定决策策略; 3）通过使种群多样性成为任务的重要组成部分，共生进化可以为更困难的问题开发解决方案，并且比标准遗传算法更有效。

Sequential decision tasks appear in many real-world domains, including control, resource allocation, routing, and scheduling. The objective of this project is to develop a new approach to sequential decision making based on symbiotic evolution of neural networks. In symbiotic evolution, a population of neurons are evolved with genetic algorithms to cooperate and form decision-making networks. Diversity is maintained in the population as part of the task and the system can find good solutions efficiently even in difficult tasks with sparse reinforcement. In the proposed project, symbiotic evolution will be analyzed both theoretically and experimentally, the algorithm will be further developed and applied to complex real-world domains such as local-area network management, and a practical high-level interface will be developed that will allow rapid application of the method to new domains. The main scientific contributions of the research are expected to be: (1) a novel, powerful method for extracting and encoding problem-specific knowledge automatically for sequential decision making, and (2) a thorough understanding of the role of diversity and cooperation in genetic algorithms. The development of the general application interface should also benefit many practical fields, including control engineering, military science, and operations management. The main hypotheses to be tested are: (1) Pattern recognition and generalization capabilities of neural networks can be used to implement effective and robust sequential decision making strategies;(2) Genetic algorithms can discover powepful problem-specific decision-making strategies even under sparse reinforcement; 3) By making population diversity an essential part of the task, symbiotic evolution can develop solutions to harder problems and do it more efficiently than standard genetic algorithms.