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

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了广泛的影响。开发这些系统的几个最强大和最有前途的方法借鉴了生物学的思想，例如，当人工神经网络被用来使计算机系统能够学习时。由生物学驱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所特有的那种激进的创新。在这个项目中，遗传编程的关键要素，如控制变异的过程，将被允许自行调整，目的是生产更强大的解决问题的计算机系统。这些系统可以应用于科学和工程的几个领域。该项目将在教育活动的背景下进行，将本科和研究生水平的研究和教育结合起来，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，使其能够更常规地为困难问题提供更具创新性的解决方案，并生产在复杂环境中表现良好的系统。这种努力背后的核心假设是，生物学的创新能力，以及生物学产生强大系统的能力，部分源于生物学自身的适应机制。自适应遗传规划系统，其中变异和选择的算法本身受制于变异和选择，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希望。拟议的项目将从这类有前途的系统开始，并将系统地对其进行测试，以便阐明一般原则，然后用于开发和应用更精细、适应性更强的算法。应用范围从第一学期编程教科书中的自动编程练习到虚拟生态系统中多细胞生物的开发，将用于测试和演示本项目中开发的系统。

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.