RI: Small: RUI: Synthesis of Robust Artificial Systems by Adaptive Genetic Programming

RI：小型：RUI：通过自适应遗传编程合成稳健的人工系统

• 批准号: 1617087
• 负责人: Lee Spector
• 金额: $ 41.89万
• 依托单位: Hampshire College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617087&HistoricalAwards=false
• 关键词: RI; Small; RUI; Synthesis; Robust

Computer-based problem-solving systems are revolutionizing many areas of science and engineering, with pervasive impacts on economic activity, human health, national security, and the advancement of science. Several of the most powerful and promising approaches to the development of these systems borrow ideas from biology, for example, when artificial neural networks are used to enable computer systems to learn. The processes of random variation and fitness-based selection motivated by biology have been particularly useful in several applications, but they have not yet produced the kind of radical innovations that are characteristic of living systems. In this project, key elements of genetic programming, such as the processes governing variation, will themselves be allowed to adapt, with the aim of producing more powerful problem-solving computer systems. These systems may have applications in several areas of science and engineering. The project will be conducted in the context of educational activities that integrate research and education across undergraduate and graduate levels, thereby providing training to a new generation of computational scientists. The primary goal of the proposed project is to enhance genetic programming technologies in ways that will allow them to more routinely produce more innovative solutions to difficult problems, and to produce systems that perform well in complex environments. The central hypothesis underlying this effort is that the innovating power of biology, and the power of biology to produce robust systems, stems in part from the fact that the adaptive mechanisms of biology themselves adapt. Self-adaptive genetic programming systems, in which the algorithms for variation and selection are themselves subject to variation and selection, have been explored for decades but have only recently begun to show practical promise for solving difficult problems. The proposed project will begin with a promising system of this type and will test it systematically, in order to elucidate general principles that will then be used to develop and apply more refined, adaptive algorithms. Applications ranging from the automatic programming of exercises in a first-semester programming textbook to the development of multicellular organisms in a virtual ecosystem will be used to test and demonstrate the systems developed in this project.

基于计算机的问题解决系统正在彻底改变科学和工程的许多领域，对经济活动、人类健康、国家安全和科学进步产生普遍影响。开发这些系统的几种最强大和最有前途的方法借鉴了生物学的思想，例如，当使用人工神经网络使计算机系统能够学习时。由生物学推动的随机变异和基于适应性的选择过程在一些应用中特别有用，但它们尚未产生生命系统特有的那种彻底的创新。在这个项目中，遗传编程的关键要素，例如控制变异的过程，本身将被允许适应，目的是产生更强大的解决问题的计算机系统。这些系统可能在科学和工程的多个领域都有应用。该项目将在整合本科生和研究生水平的研究和教育的教育活动背景下进行，从而为新一代计算科学家提供培训。拟议项目的主要目标是增强遗传编程技术，使它们能够更常规地为难题提供更具创新性的解决方案，并产生在复杂环境中表现良好的系统。这项工作的核心假设是，生物学的创新能力，以及生物学产生强大系统的能力，部分源于生物学本身的适应性机制这一事实。自适应遗传编程系统，其中变异和选择的算法本身也受到变异和选择的影响，已经被探索了几十年，但直到最近才开始显示出解决难题的实际前​​景。拟议的项目将从此类有前途的系统开始，并对它进行系统测试，以阐明一般原则，然后将这些原则用于开发和应用更精细的自适应算法。从第一学期编程教科书中练习的自动编程到虚拟生态系统中多细胞生物的开发等应用程序将用于测试和演示该项目中开发的系统。