Symbiotic Evolution of Neural Networks in Sequential Decision Tasks

神经网络在顺序决策任务中的共生进化

• 批准号: 9504317
• 负责人: Risto Miikkulainen
• 金额: $ 25.95万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-09-15 至 1999-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9504317&HistoricalAwards=false
• 关键词: Symbiotic; Evolution; Neural; Networks; Sequential

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.

顺序决策任务出现在许多现实领域，包括控制、资源分配、路由和调度。本项目的目标是开发一种新的基于神经网络共生进化的序贯决策方法。在共生进化中，一群神经元与遗传算法一起进化，以进行协作并形成决策网络。作为任务的一部分，种群中保持了多样性，即使在具有稀疏强化的困难任务中，系统也能有效地找到良好的解决方案。在提出的项目中，将从理论和实验两方面对共生进化进行分析，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，使该方法能够快速应用到新的领域。这项研究的主要科学贡献有望是：(1)为顺序决策提供一种新的、强大的方法来自动提取和编码特定问题的知识，以及(2)彻底理解多样性和合作在遗传算法中的作用。通用应用程序接口的开发也将使许多实用领域受益，包括控制工程、军事科学和作战管理。要检验的主要假设是：(1)神经网络的模式识别和泛化能力可以用于实现有效和健壮的顺序决策策略；(2)遗传算法即使在稀疏强化的情况下也可以发现强大的特定于问题的决策策略；3)通过将种群多样性作为任务的重要组成部分，共生进化可以开发出更难解决的问题的解决方案，并且比标准遗传算法更有效。